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REVIEW 5 major objections 5 minor 143 references

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Prompted ChatGPT and Gemini reach the previous best only on requirements question answering, with ChatGPT edging past it at 0.91 versus 0.90 F1.

desk verdict Useful four-task evaluation of ChatGPT and Gemini for requirements engineering, but the one headline claim—ChatGPT beating AlBERT on QA—is not supported as reported. read the letter →

arxiv 2412.00959 v1 pith:GBQ23RZU submitted 2024-12-01 cs.SE cs.AI

Summra Saleem , Muhammad Nabeel Asim , Ludger Van Elst , Andreas Dengel This is my paper · ORCID
classification cs.SEcs.AI
keywords requirementsengineeringgenerativelanguagemodelsChatGPTGeminipromptclassificationnamedentityrecognitionquestionanswering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks whether two general-purpose generative language models, ChatGPT and Gemini, can take over four requirements engineering jobs: extracting requirements from documents, classifying them as functional or non-functional, tagging named entities, and answering questions about requirements. It reports that, except for question answering, both models lag the best task-specific predictors on the same public benchmarks. ChatGPT reaches 0.91 F1 on the REQuestA question-answering benchmark, just above the reported best prior score of 0.90, while Gemini reaches 0.88. The authors also find that Gemini needs more domain-specific prompt detail than ChatGPT, and that richer prompts help on some tasks but push predictions toward one class on others. If the findings hold, the current practical value of generative LLMs in requirements engineering lies mainly in question answering, not in extraction, classification, or NER.

What carries the argument

The machinery is a three-level prompt protocol. Level 1 gives the model only the task query, Level 2 adds background definitions, and Level 3 adds definitions plus worked examples; the same three prompts are fed to both models for each task. The outputs are scored with standard measures (accuracy, precision, recall, F1, and for QA also ROUGE, BLEU, and METEOR). This protocol is what lets the authors separate model capability from prompt informativeness, and it underlies both main conclusions: Gemini needs more expert knowledge in its prompts than ChatGPT, and adding domain-specific keywords can steer predictions toward one class.

What would settle it

Run ChatGPT and Gemini on the REQuestA question-answer pairs with the answer-bearing passages withheld, and separately on a contamination-checked version of the benchmark; if F1 drops below the prior best of 0.90, the claimed QA advantage is an artifact of in-prompt retrieval or memorization. For extraction, classification, and NER, retrain the prior predictors and the LLMs under identical train/test splits, evaluation scripts, and decoding settings; if the F1 gaps shrink to near zero, the reported underperformance is an artifact of protocol mismatch rather than a real model limitation.

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Extended reading notes

Core claim

The central claim is that, across four public benchmarks, ChatGPT and Gemini underperform the current state-of-the-art predictors for requirements extraction, requirements classification, and named entity recognition, but match or slightly beat them for requirements question answering. On extraction (Pure dataset) the best prior F1 is 0.86, while ChatGPT and Gemini score 0.76 and 0.77; on classification (PROMISE) the best prior F1 is 0.96, while both score 0.78; on NER (Aerospace) the best prior F1 is 0.92, while ChatGPT scores 0.36 and Gemini 0.25. On REQuestA, ChatGPT's best F1 is 0.91 against 0.90 for the best prior predictor, and Gemini reaches 0.88. The paper concludes that generative models are currently useful for building requirements question-answering applications, while traditional machine/deep learning models or foundational language models remain the better choice for the other three tasks.

Load-bearing premise

The load-bearing premise is that the F1 scores previously reported for task-specific predictors can be fairly compared with the prompted LLM results, even though the earlier systems were trained or fine-tuned under different split and evaluation protocols, and that ChatGPT and Gemini had not already memorized the public benchmark texts during pretraining.

Editorial extensions

If this is right

  • For requirements question answering, ChatGPT is a viable option: on REQuestA its best F1 of 0.91 marginally exceeds the best prior predictor's 0.90.
  • For extraction, classification, and NER, practitioners should expect fine-tuned task-specific predictors to beat prompted ChatGPT and Gemini by wide margins (0.10, 0.18, and 0.56 F1, respectively).
  • Prompt content matters: generic prompts sometimes outperform knowledge-rich prompts, and domain-specific keywords bias predictions toward a particular class, so prompt design should be treated as part of the evaluation.
  • Gemini needs more carefully engineered, domain-specific prompts to reach its best results, while ChatGPT is comparatively better with generic prompts.
  • Repeated runs on the same extraction samples produce inconsistent labels for both models, so single-shot evaluations are unreliable and averaging over multiple iterations is advisable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension is to vary how the answer-bearing text is supplied in the QA prompts; the paper's gains at prompt level 3 may come largely from giving the model the relevant passage, so retrieval quality, not generative skill, could be the deciding factor.
  • The observed sensitivity to prompt wording suggests that automatic hard- or soft-prompt optimization could narrow the classification and NER gaps; the paper names prompt optimization as a future direction but does not test it.
  • A contamination check would settle whether the QA advantage transfers to unseen requirements documents: if REQuestA text appeared in pretraining, the reported 0.91 F1 would be inflated and would not generalize.
  • The reproducibility results imply that any LLM benchmark report should include variance across repeated API calls; the paper reports such variance for 30 extraction samples only, not for the other tasks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. This manuscript reports a case study comparing ChatGPT-3.5 and Gemini on four requirements engineering tasks: requirements extraction (Pure), requirements classification (PROMISE), named entity recognition (Aerospace), and question answering (REQuestA). For each task, the authors design three prompt types with increasing levels of domain knowledge, evaluate the models using accuracy/F1 and related metrics, and compare against previously published task-specific predictors. The headline findings are that both generative models underperform state-of-the-art predictors on extraction, classification, and NER, while ChatGPT reaches F1=0.91 on REQuestA QA, slightly above the existing best AlBERT result of 0.90, and that Gemini requires more careful prompt engineering than ChatGPT.

Significance. The study is useful as a broad empirical snapshot of two widely used closed-source generative models across standard RE benchmarks. It contributes a prompt-level comparison, error analysis via Venn diagrams, and a small reproducibility analysis, and it goes beyond anecdote by evaluating on public datasets. However, the paper's only positive headline result—state-of-the-art QA performance—rests on a comparison that is confounded by unequal access to answer-bearing context and by possible pretraining contamination. Because the practical conclusion in Sections 5 and 6 (that generative models are useful for QA) depends on this single result, the central claim is not yet established; additional experiments or clearly stated limitations are required.

major comments (5)
  1. [Section 4.3.4, Table 7, Figure 6] The QA comparison is not apples-to-apples: the baseline predictors (MiniLM, RoBERTa, ELECTRA, DistilBERT, BERT, AlBERT) were fine-tuned on REQuestA training pairs, whereas ChatGPT and Gemini were prompted, and Section 4.1.1 states that prompts 2 and 3 contain the text that contains the answers. This gives the generative models an extractive advantage and invalidates the claim that ChatGPT 'achieves state-of-the-art performance' in the question answering task. Please rerun the comparison with baselines that have the same passage access, or evaluate the GLMs without answer-bearing passages and with fine-tuned variants, or clearly reframe the claim as 'prompted extractive QA' rather than state-of-the-art.
  2. [Section 3.2.4 and Section 4.3.4] Pretraining contamination is not addressed. REQuestA is constructed from public SRS documents and Wikipedia articles, and ChatGPT-3.5's training data likely includes at least the Wikipedia content; retrieval from memorized passages can inflate QA F1. The authors neither test for text overlap between the benchmark and pretraining data nor discuss this threat. A contamination analysis (for example, n-gram overlap statistics or evaluation on newly collected held-out QA pairs) is needed before the 0.91 vs 0.90 result can be treated as evidence of general QA capability.
  3. [Section 3.1, Section 4, and Section 4.1.1] The evaluation is not reproducible as reported. Section 3.1 refers to supplementary Tables 1-4 for the exact prompts, but the supplementary file is not included with the reviewed version; Section 4 mentions a rule-based content and tags extractor for NER but does not provide the script; and Section 4 does not give API versions or access dates for ChatGPT-3.5 and Gemini. Without the prompts, the NER parsing script, and model version/date information, the prompt-dependence results cannot be audited or replicated.
  4. [Abstract and Section 3.2.4] There is an inconsistency in the reported size of the REQuestA dataset: the abstract states 300 QA pairs, while Section 3.2.4 reports 387 QA pairs. If the experiments use 387 pairs, the abstract is incorrect; if they use 300, the comparison to Ezzini et al.'s baseline scores may be on a different subset. Please reconcile the number and state exactly which split or subset was used for both the GLMs and the baselines.
  5. [Section 5 and Tables 4-6] The conclusion that 'for both classification and NER tagging tasks, basic prompts outperform knowledge rich prompts' is not supported by the reported data. Gemini's extraction accuracy increases monotonically with prompt level (0.746, 0.753, 0.790) and classification accuracy is flat between prompts 2 and 3; ChatGPT's best NER F1 occurs at prompt 2, not prompt 1. Please restrict the conclusion to the tasks and models for which the data actually show the pattern, and temper the generalization accordingly.
minor comments (5)
  1. [Section 3.3] The text says the QA evaluation uses '8 different evaluation measures' but then lists nine: precision, recall, F1-score, ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-S, METEOR, and BLEU.
  2. [Figure 5] The legend contains 'MoW+SVM', which appears to be a typo for 'BoW+SVM'.
  3. [Section 4.3.1 vs Section 4.3] Section 4.3.1 says the comparison includes '3 existing predictors' while Section 4.3's summary says 1 predictor for requirements extraction; please clarify that the three predictors are all from Ivanov et al. [60].
  4. [Tables 4-7] The capitalization of ROUGE is inconsistent across the manuscript (for example, 'Rouge-1', 'Rouge-2', and 'Rouge-L' versus 'ROUGE-S'); please standardize.
  5. [Section 4.2] The reproducibility analysis is based on only 30 samples from a single dataset; please state this limitation explicitly in the text rather than leaving the reader to infer it.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: empirical benchmark comparison with independent baselines.

full rationale

This paper is an empirical measurement study, not a derivation: ChatGPT and Gemini are prompted on public benchmarks and their outputs are scored against ground truth, so no result is forced by construction. The only self-citation is FnReq-Net [11], used as a comparison baseline in the requirements-classification plot; the LLM outputs are independent of how that model was built, and the baseline is published and externally falsifiable, so it does not constitute load-bearing self-citation. The central QA claim (F1 0.91 vs 0.90) is an evaluation result, not a fitted parameter renamed as a prediction. Concerns about pretraining contamination and unequal evaluation protocols (zero-shot prompting vs fine-tuned baselines) are validity and reproducibility risks, not circularity, and therefore do not affect the circularity score.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The study relies on empirical evaluation rather than a mathematical derivation. The main unstated assumptions are comparability of literature baselines, reliability of the NER output parser, and absence of pretraining contamination. There are no fitted numeric parameters and no new postulated entities.

assumptions (3)
  • domain assumption Reported state-of-the-art results from cited papers are directly comparable to the LLM results.
    Section 4.3 compares LLM performance with literature-reported numbers without re-running baselines under identical conditions; this assumes metric definitions and data splits match.
  • domain assumption The rule-based extractor correctly parses LLM NER output.
    Section 4 states that a rule-based content and tags extractor script was written for NER, but the script and its validation are not provided; the low NER scores depend on this parsing.
  • domain assumption The benchmark test sets have not been seen by ChatGPT or Gemini during pretraining.
    Unstated in Sections 3.2 and 4.3; public RE datasets like PROMISE could be in pretraining corpora, which would inflate QA performance.

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Cite this review

Pith. "Pith review of Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations." pith.science (2026). https://pith.science/paper/GBQ23RZU

@misc{pith2026241200959,
  author       = {Pith},
  title        = {Pith review of: Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GBQ23RZU}},
  note         = {Machine review of arXiv:2412.00959}
}
read the original abstract

Traditional language models have been extensively evaluated for software engineering domain, however the potential of ChatGPT and Gemini have not been fully explored. To fulfill this gap, the paper in hand presents a comprehensive case study to investigate the potential of both language models for development of diverse types of requirement engineering applications. It deeply explores impact of varying levels of expert knowledge prompts on the prediction accuracies of both language models. Across 4 different public benchmark datasets of requirement engineering tasks, it compares performance of both language models with existing task specific machine/deep learning predictors and traditional language models. Specifically, the paper utilizes 4 benchmark datasets; Pure (7,445 samples, requirements extraction),PROMISE (622 samples, requirements classification), REQuestA (300 question answer (QA) pairs) and Aerospace datasets (6347 words, requirements NER tagging). Our experiments reveal that, in comparison to ChatGPT, Gemini requires more careful prompt engineering to provide accurate predictions. Moreover, across requirement extraction benchmark dataset the state-of-the-art F1-score is 0.86 while ChatGPT and Gemini achieved 0.76 and 0.77,respectively. The State-of-the-art F1-score on requirements classification dataset is 0.96 and both language models 0.78. In name entity recognition (NER) task the state-of-the-art F1-score is 0.92 and ChatGPT managed to produce 0.36, and Gemini 0.25. Similarly, across question answering dataset the state-of-the-art F1-score is 0.90 and ChatGPT and Gemini managed to produce 0.91 and 0.88 respectively. Our experiments show that Gemini requires more precise prompt engineering than ChatGPT. Except for question-answering, both models under-perform compared to current state-of-the-art predictors across other tasks.

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Works this paper leans on

143 extracted references · 58 canonical work pages

  1. [1]

    In: Text Analysis for the Social Sciences, pp

    Palmquist, M.E., Carley, K.M., Dale, T.A.: Applications of computer- aided text analysis: Analyzing literary and nonliterary texts. In: Text Analysis for the Social Sciences, pp. 171–190. Routledge, ??? (2020) Springer Nature 2021 LATEX template Generative Language Models 25

    2020

  2. [2]

    In: Proceedings of Second Interna- tional Conference on Computing, Communications, and Cyber-Security: IC4S 2020, pp

    Oza, P., Sharma, P., Patel, S.: Machine learning applications for computer-aided medical diagnostics. In: Proceedings of Second Interna- tional Conference on Computing, Communications, and Cyber-Security: IC4S 2020, pp. 377–392 (2021). Springer

    2021

  3. [3]

    Physica Medica 83, 9–24 (2021)

    Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Inter- lenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N.C., Sardanelli, F.: Ai applications to medical images: From machine learning to deep learning. Physica Medica 83, 9–24 (2021)

    2021

  4. [4]

    Journal of the American Medical Informatics Association 27(3), 491–497 (2020)

    Reddy, S., Allan, S., Coghlan, S., Cooper, P.: A governance model for the application of ai in health care. Journal of the American Medical Informatics Association 27(3), 491–497 (2020)

    2020

  5. [5]

    In: Product-Focused Software Process Improvement: 10th International Conference, PROFES 2009, Oulu, Finland, June 15-17,

    Petersen, K., Wohlin, C., Baca, D.: The waterfall model in large-scale development. In: Product-Focused Software Process Improvement: 10th International Conference, PROFES 2009, Oulu, Finland, June 15-17,

    2009

  6. [6]

    ACM SIGSOFT Software Engineering Notes 35(3), 8–13 (2010)

    Ruparelia, N.B.: Software development lifecycle models. ACM SIGSOFT Software Engineering Notes 35(3), 8–13 (2010)

    2010

  7. [7]

    Computer 21(5), 61–72 (1988)

    Boehm, B.W.: A spiral model of software development and enhancement. Computer 21(5), 61–72 (1988)

    1988

  8. [8]

    arXiv preprint arXiv:1709.08439 (2017)

    Abrahamsson, P., Salo, O., Ronkainen, J., Warsta, J.: Agile soft- ware development methods: Review and analysis. arXiv preprint arXiv:1709.08439 (2017)

    arXiv 2017

Show all 143 references
  1. [9]

    Auerbach Publications, ??? (2022)

    Laplante, P.A., Kassab, M.: Requirements Engineering for Software and Systems. Auerbach Publications, ??? (2022)

    2022

  2. [10]

    In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp

    Pandey, D., Suman, U., Ramani, A.K.: An effective requirement engi- neering process model for software development and requirements management. In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 287–291 (2010). IEEE

    2010

  3. [11]

    Journal of King Saud University-Computer and Information Sciences 35(8), 101665 (2023)

    Saleem, S., Asim, M.N., Van Elst, L., Dengel, A.: Fnreq-net: A hybrid computational framework for functional and non-functional require- ments classification. Journal of King Saud University-Computer and Information Sciences 35(8), 101665 (2023)

    2023

  4. [12]

    In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp

    Hey, T., Keim, J., Koziolek, A., Tichy, W.F.: Norbert: Transfer learn- ing for requirements classification. In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp. 169–179 (2020). IEEE Springer Nature 2021 LATEX template 26 Generative Language Models

    2020

  5. [13]

    In: 2017 IEEE 25th International Requirements Engineering Conference (RE), pp

    Kurtanovi´ c, Z., Maalej, W.: Automatically classifying functional and non-functional requirements using supervised machine learning. In: 2017 IEEE 25th International Requirements Engineering Conference (RE), pp. 490–495 (2017). Ieee

    2017

  6. [14]

    Mahalakshmi, G., Vijayan, V., Antony, B.: Named entity recognition for automated test case generation. Int. Arab J. Inf. Technol.15(1), 112–120 (2018)

    2018

  7. [15]

    Design Engineering, 2662–2678 (2021)

    Nagpal, M.D., Malik, K., Kalia, A.: A comprehensive analysis of requirement engineering utilizing machine learning techniques. Design Engineering, 2662–2678 (2021)

    2021

  8. [16]

    Althanoon, A.A.A., Younis, Y.S.: Supporting classification of software requirements system using intelligent technologies algorithms (2021)

    2021

  9. [17]

    symmetry 12(10), 1601 (2020)

    Rahimi, N., Eassa, F., Elrefaei, L.: An ensemble machine learning tech- nique for functional requirement classification. symmetry 12(10), 1601 (2020)

    2020

  10. [18]

    In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp

    Haque, M.A., Rahman, M.A., Siddik, M.S.: Non-functional require- ments classification with feature extraction and machine learning: An empirical study. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–5 (2019). IEEE

    2019

  11. [19]

    Information Technology and Control 48(3), 432–445 (2019)

    T´ oth, L., Vid´ acs, L.: Comparative study of the performance of var- ious classifiers in labeling non-functional requirements. Information Technology and Control 48(3), 432–445 (2019)

    2019

  12. [20]

    In: 2017 5th International Conference in Soft- ware Engineering Research and Innovation (CONISOFT), pp

    Navarro-Almanza, R., Juarez-Ramirez, R., Licea, G.: Towards sup- porting software engineering using deep learning: A case of software requirements classification. In: 2017 5th International Conference in Soft- ware Engineering Research and Innovation (CONISOFT), pp. 116–120 (2...

    2017

  13. [21]

    Springer, ??? (2023)

    Liu, Z., Lin, Y., Sun, M.: Representation Learning for Natural Language Processing. Springer, ??? (2023)

    2023

  14. [22]

    Proceed- ings of the IEEE 109(5), 612–634 (2021)

    Sch¨ olkopf, B., Locatello, F., Bauer, S., Ke, N.R., Kalchbrenner, N., Goyal, A., Bengio, Y.: Toward causal representation learning. Proceed- ings of the IEEE 109(5), 612–634 (2021)

    2021

  15. [23]

    International Journal of Approximate Reasoning 103, 1–10 (2018) Springer Nature 2021 LATEX template Generative Language Models 27

    Pham, D.-H., Le, A.-C.: Exploiting multiple word embeddings and one- hot character vectors for aspect-based sentiment analysis. International Journal of Approximate Reasoning 103, 1–10 (2018) Springer Nature 2021 LATEX template Generative Language Models 27

    2018

  16. [24]

    BMC medical informatics and decision making 20, 1–7 (2020)

    Xiong, Y., Chen, S., Qin, H., Cao, H., Shen, Y., Wang, X., Chen, Q., Yan, J., Tang, B.: Distributed representation and one-hot representation fusion with gated network for clinical semantic textual similarity. BMC medical informatics and decision making 20, 1–7 (2020)

    2020

  17. [25]

    Neurocomputing 177, 257–265 (2016)

    Qin, P., Xu, W., Guo, J.: A novel negative sampling based on tfidf for learning word representation. Neurocomputing 177, 257–265 (2016)

    2016

  18. [26]

    In: 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), pp

    Liu, C.-z., Sheng, Y.-x., Wei, Z.-q., Yang, Y.-Q.: Research of text classi- fication based on improved tf-idf algorithm. In: 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), pp. 218–222 (2018). IEEE

    2018

  19. [27]

    Innovative Data Communication Tech- nologies and Application: Proceedings of ICIDCA 2020, 267–281 (2021)

    Selva Birunda, S., Kanniga Devi, R.: A review on word embedding techniques for text classification. Innovative Data Communication Tech- nologies and Application: Proceedings of ICIDCA 2020, 267–281 (2021)

    2021

  20. [28]

    Journal of big data 2(1), 1–21 (2015)

    Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. Journal of big data 2(1), 1–21 (2015)

    2015

  21. [29]

    Artificial intelligence in medicine 117, 102083 (2021)

    Kraljevic, Z., Searle, T., Shek, A., Roguski, L., Noor, K., Bean, D., Mascio, A., Zhu, L., Folarin, A.A., Roberts, A., et al.: Multi-domain clinical natural language processing with medcat: the medical concept annotation toolkit. Artificial intelligence in medicine 117, 102083 (2021)

    2021

  22. [30]

    IEEE access 7, 53040–53065 (2019)

    Shrestha, A., Mahmood, A.: Review of deep learning algorithms and architectures. IEEE access 7, 53040–53065 (2019)

    2019

  23. [31]

    arXiv preprint arXiv:2103.06268 (2021)

    Hendrycks, D., Burns, C., Chen, A., Ball, S.: Cuad: An expert-annotated nlp dataset for legal contract review. arXiv preprint arXiv:2103.06268 (2021)

    2021 arXiv

  24. [32]

    Cambridge University Press, ??? (2020)

    Yang, Q., Zhang, Y., Dai, W., Pan, S.J.: Transfer Learning. Cambridge University Press, ??? (2020)

    2020

  25. [33]

    In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pp

    Ruder, S., Peters, M.E., Swayamdipta, S., Wolf, T.: Transfer learning in natural language processing. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pp. 15–18 (2019)

    2019

  26. [34]

    APSIPA transactions on signal and information processing 8, 19 (2019)

    Wang, B., Wang, A., Chen, F., Wang, Y., Kuo, C.-C.J.: Evaluating word embedding models: Methods and experimental results. APSIPA transactions on signal and information processing 8, 19 (2019)

    2019

  27. [35]

    Computing 102, 717–740 (2020) Springer Nature 2021 LATEX template 28 Generative Language Models

    Wang, S., Zhou, W., Jiang, C.: A survey of word embeddings based on deep learning. Computing 102, 717–740 (2020) Springer Nature 2021 LATEX template 28 Generative Language Models

    2020

  28. [36]

    Simon and Schuster, ??? (2021)

    Azunre, P.: Transfer Learning for Natural Language Processing. Simon and Schuster, ??? (2021)

    2021

  29. [37]

    Neural Computing and Applications, 1–24 (2023)

    Mehmood, F., Ghafoor, H., Asim, M.N., Ghani, M.U., Mahmood, W., Dengel, A.: Passion-net: a robust precise and explainable predictor for hate speech detection in roman urdu text. Neural Computing and Applications, 1–24 (2023)

    2023

  30. [38]

    Natural Language Processing Journal 4, 100020 (2023)

    Jansen, B.J., Jung, S.-g., Salminen, J.: Employing large language mod- els in survey research. Natural Language Processing Journal 4, 100020 (2023)

    2023

  31. [39]

    In: Recent Trends in Analysis of Images, Social Networks and Texts: 10th International Conference, AIST 2021, Tbilisi, Georgia, December 16–18, 2021, Revised Selected Papers, pp

    Ivanov, V., Sadovykh, A., Naumchev, A., Bagnato, A., Yakovlev, K.: Extracting software requirements from unstructured documents. In: Recent Trends in Analysis of Images, Social Networks and Texts: 10th International Conference, AIST 2021, Tbilisi, Georgia, December 16–18, 2021...

    2022

  32. [40]

    Lahmer, M., et al.: Automating analysis process of agile methodology using artificial intelligence

  33. [41]

    Nature Ecology & Evolution 7(1), 62–70 (2023)

    Sasmito, S.D., Basyuni, M., Kridalaksana, A., Saragi-Sasmito, M.F., Lovelock, C.E., Murdiyarso, D.: Challenges and opportunities for achiev- ing sustainable development goals through restoration of indonesia’s mangroves. Nature Ecology & Evolution 7(1), 62–70 (2023)

    2023

  34. [42]

    Futures 146, 103087 (2023)

    Arora-Jonsson, S.: The sustainable development goals: A universalist promise for the future. Futures 146, 103087 (2023)

    2023

  35. [43]

    In: Cana- dian Conference on AI (2021)

    Malik, G., Cevik, M., Khedr, Y., Parikh, D., Basar, A.: Named entity recognition on software requirements specification documents. In: Cana- dian Conference on AI (2021)

    2021

  36. [44]

    arXiv preprint arXiv:2302.04793 (2023)

    Ezzini, S., Abualhaija, S., Arora, C., Sabetzadeh, M.: Ai-based question answering assistance for analyzing natural-language requirements. arXiv preprint arXiv:2302.04793 (2023)

    2023 arXiv

  37. [45]

    In: ENASE, pp

    Vijayvargiya, S., Kumar, L., Neti, L.B.M., Misra, S., Krishna, A., Pad- manabhuni, S.: Software engineering comments sentiment analysis using lstm with various padding sizes. In: ENASE, pp. 396–403 (2023)

    2023

  38. [46]

    Amini-Salehi, E., Bozorgi, A., Keivanlou, M.-H., Joukar, F., Dave, T., Alotaibi, A., Bakhsi, A., Hassanipour, S.: Do you really want to use chat-gpt for paraphrasing your texts? Available at SSRN 4514430

  39. [47]

    In: Proceedings of the International Conference on Springer Nature 2021 LATEX template Generative Language Models 29 Recent Advances in Natural Language Processing (RANLP 2021), pp

    Neto, P.B., Ruiz, E.E.S.: Split-and-rephrase in a cross-lingual manner: a complete pipeline. In: Proceedings of the International Conference on Springer Nature 2021 LATEX template Generative Language Models 29 Recent Advances in Natural Language Processing (RANLP 2021), pp. 15...

    2021

  40. [48]

    medRxiv, 2023–02 (2023)

    Sallam, M.: The utility of chatgpt as an example of large language models in healthcare education, research and practice: Systematic review on the future perspectives and potential limitations. medRxiv, 2023–02 (2023)

    2023

  41. [49]

    Jama 330(9), 866–869 (2023)

    Shah, N.H., Entwistle, D., Pfeffer, M.A.: Creation and adoption of large language models in medicine. Jama 330(9), 866–869 (2023)

    2023

  42. [50]

    Southeast Europe Journal of Soft Computing 12(1), 13–41 (2023)

    Mufti´ c, F., Kaduni´ c, M., Muˇ sinbegovi´ c, A., Abd Almisreb, A.: Exploring medical breakthroughs: A systematic review of chatgpt applications in healthcare. Southeast Europe Journal of Soft Computing 12(1), 13–41 (2023)

    2023

  43. [51]

    In: Proceedings of the Fourth ACM International Conference on AI in Finance, pp

    Li, Y., Wang, S., Ding, H., Chen, H.: Large language models in finance: A survey. In: Proceedings of the Fourth ACM International Conference on AI in Finance, pp. 374–382 (2023)

    2023

  44. [52]

    Finance Research Letters 53, 103662 (2023)

    Dowling, M., Lucey, B.: Chatgpt for (finance) research: The bananarama conjecture. Finance Research Letters 53, 103662 (2023)

    2023

  45. [53]

    Available at SSRN 4603206 (2023)

    Rane, N.: Role and challenges of chatgpt and similar generative artifi- cial intelligence in finance and accounting. Available at SSRN 4603206 (2023)

    2023

  46. [54]

    Learning and individual differences 103, 102274 (2023)

    Kasneci, E., Seßler, K., K¨ uchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., G¨ unnemann, S., H¨ ullermeier, E., et al.: Chatgpt for good? on opportunities and challenges of large language models for education. Learning and individual differences 1...

    2023

  47. [55]

    A practical guide to sentiment analysis, 107–134 (2017)

    Wang, H., Zhai, C.: Generative models for sentiment analysis and opinion mining. A practical guide to sentiment analysis, 107–134 (2017)

    2017

  48. [56]

    In: 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), pp

    Yeow, J.S., Rana, M.E., Majid, N.A.A.: An automated model of soft- ware requirement engineering using gpt-3.5. In: 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), pp. 1746–1755 (2024). IEEE

    2024

  49. [57]

    In: 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR), pp

    Wu, L., Zhao, Y., Hou, X., Liu, T., Wang, H.: Chatgpt chats decoded: Uncovering prompt patterns for superior solutions in software develop- ment lifecycle. In: 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR), pp. 142–146 (2024). IEEE

    2024

  50. [58]

    In: 2023 International Conference on Computational Science and Springer Nature 2021 LATEX template 30 Generative Language Models Computational Intelligence (CSCI), pp

    Hamdi, M., Kim, L.D.: A prompt-based approach for software develop- ment. In: 2023 International Conference on Computational Science and Springer Nature 2021 LATEX template 30 Generative Language Models Computational Intelligence (CSCI), pp. 1612–1614 (2023). IEEE

    2023

  51. [59]

    245–255 (2024)

    Alter, S.: Could a large language model contribute significantly to requirements analysis? In: International Conference on Business Process Modeling, Development and Support, pp. 245–255 (2024). Springer

    2024

  52. [60]

    In: International Conference on Analysis of Images, Social Networks and Texts, pp

    Ivanov, V., Sadovykh, A., Naumchev, A., Bagnato, A., Yakovlev, K.: Extracting software requirements from unstructured documents. In: International Conference on Analysis of Images, Social Networks and Texts, pp. 17–29 (2021). Springer

    2021

  53. [61]

    In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp

    Sainani, A., Anish, P.R., Joshi, V., Ghaisas, S.: Extracting and classi- fying requirements from software engineering contracts. In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp. 147–157 (2020). IEEE

    2020

  54. [62]

    Entropy 22(9), 1057 (2020)

    Dias Canedo, E., Cordeiro Mendes, B.: Software requirements classifica- tion using machine learning algorithms. Entropy 22(9), 1057 (2020)

    2020

  55. [63]

    In: Journal of Physics: Conference Series, vol

    Tiun, S., Mokhtar, U., Bakar, S., Saad, S.: Classification of functional and non-functional requirement in software requirement using word2vec and fast text. In: Journal of Physics: Conference Series, vol. 1529, p. 042077 (2020). IOP Publishing

    2020

  56. [64]

    Journal of Software: Evolution and Process, 2430 (2022)

    Kaur, K., Kaur, P.: Sabdm: A self-attention based bidirectional-rnn deep model for requirements classification. Journal of Software: Evolution and Process, 2430 (2022)

    2022

  57. [65]

    arXiv preprint arXiv:2211.05286 (2022)

    Khayashi, F., Jamasb, B., Akbari, R., Shamsinejadbabaki, P.: Deep learning methods for software requirement classification: A performance study on the pure dataset. arXiv preprint arXiv:2211.05286 (2022)

    2022 arXiv

  58. [66]

    Entropy 23(10), 1264 (2021)

    Rahimi, N., Eassa, F., Elrefaei, L.: One-and two-phase software require- ment classification using ensemble deep learning. Entropy 23(10), 1264 (2021)

    2021

  59. [67]

    Bioinformatics 37(15), 2112–2120 (2021)

    Ji, Y., Zhou, Z., Liu, H., Davuluri, R.V.: Dnabert: pre-trained bidirec- tional encoder representations from transformers model for dna-language in genome. Bioinformatics 37(15), 2112–2120 (2021)

    2021

  60. [68]

    arXiv preprint arXiv:2112.00699 (2021)

    F´ avero, E.M.D.B., Casanova, D.: Bert se: A pre-trained lan- guage representation model for software engineering. arXiv preprint arXiv:2112.00699 (2021)

    2021 arXiv

  61. [69]

    In: 2020 IEEE Interna- tional Conference on Smart Cloud (SmartCloud), pp

    Duan, J., Zhao, H., Zhou, Q., Qiu, M., Liu, M.: A study of pre-trained language models in natural language processing. In: 2020 IEEE Interna- tional Conference on Smart Cloud (SmartCloud), pp. 116–121 (2020). Springer Nature 2021 LATEX template Generative Language Models 31 IEEE

    2020

  62. [70]

    arXiv preprint arXiv:1810.04805 (2018)

    Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

    2018 arXiv

  63. [71]

    arXiv preprint arXiv:1907.11692 (2019)

    Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

    2019 arXiv

  64. [72]

    Advances in neural information processing systems 32 (2019)

    Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: Generalized autoregressive pretraining for language understand- ing. Advances in neural information processing systems 32 (2019)

    2019

  65. [73]

    arXiv preprint arXiv:1909.11942 (2019)

    Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: A lite bert for self-supervised learning of language representa- tions. arXiv preprint arXiv:1909.11942 (2019)

    2019 arXiv

  66. [74]

    arXiv preprint arXiv:1910.01108 (2019)

    Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 (2019)

    2019 arXiv

  67. [75]

    IEEE Access 10, 30080–30090 (2022)

    Li, G., Zheng, C., Li, M., Wang, H.: Automatic requirements classifica- tion based on graph attention network. IEEE Access 10, 30080–30090 (2022)

    2022

  68. [76]

    International Journal of Computer Science and Network Security 8(2), 339–344 (2008)

    Mansouri, A., Affendey, L.S., Mamat, A.: Named entity recognition approaches. International Journal of Computer Science and Network Security 8(2), 339–344 (2008)

    2008

  69. [77]

    Journal of Systems and Software 165, 110572 (2020)

    Zhou, C., Li, B., Sun, X.: Improving software bug-specific named entity recognition with deep neural network. Journal of Systems and Software 165, 110572 (2020)

    2020

  70. [78]

    Technical report, Department of Computer Science, Virginia Polytechnic Institute & State

    Kanan, T., Ayoub, S., Saif, E., Kanaan, G., Chandrasekarar, P., Fox, E.A.: Extracting named entities using named entity recognizer and gen- erating topics using latent dirichlet allocation algorithm for arabic news articles. Technical report, Department of Computer Science, Vi...

    2015

  71. [79]

    Arslan, M., Cruz, C.: Extracting business insights through dynamic topic modeling and ner (2022)

    2022

  72. [80]

    arXiv preprint arXiv:2005.01634 (2020)

    Tabassum, J., Maddela, M., Xu, W., Ritter, A.: Code and named entity recognition in stackoverflow. arXiv preprint arXiv:2005.01634 (2020)

    2020 arXiv

  73. [81]

    In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), pp

    Li, N., Zheng, L., Wang, Y., Wang, B.: Feature-specific named entity Springer Nature 2021 LATEX template 32 Generative Language Models recognition in software development social content. In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), pp. 175–182...

    2019

  74. [82]

    In: 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), pp

    Herwanto, G.B., Quirchmayr, G., Tjoa, A.M.: A named entity recogni- tion based approach for privacy requirements engineering. In: 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), pp. 406–411 (2021). IEEE

    2021

  75. [83]

    In: Proceedings of the 26th Conference on Program Comprehension, pp

    Zhou, C., Li, B., Sun, X., Guo, H.: Recognizing software bug-specific named entity in software bug repository. In: Proceedings of the 26th Conference on Program Comprehension, pp. 108–119 (2018)

    2018

  76. [84]

    In: 2022 2nd International Conference on Intelligent Technologies (CONIT), pp

    Vineetha, K., Samuel, P.: A multinomial na ¨ ıve bayes classifier for identifying actors and use cases from software requirement specifica- tion documents. In: 2022 2nd International Conference on Intelligent Technologies (CONIT), pp. 1–5 (2022). IEEE

    2022

  77. [85]

    International Journal of Advanced Computer Science and Applications (IJACSA) (2021)

    Imam, A.T., Alhroob, A., Alzyadat, W.: Svm machine learning classi- fier to automate the extraction of srs elements. International Journal of Advanced Computer Science and Applications (IJACSA) (2021)

    2021

  78. [86]

    In: 2022 2nd Asian Con- ference on Innovation in Technology (ASIANCON), pp

    Vineetha, K., Samuel, P.: A passive-aggressive classifier for finding actors and use cases from requirement documents. In: 2022 2nd Asian Con- ference on Innovation in Technology (ASIANCON), pp. 1–5 (2022). IEEE

    2022

  79. [87]

    In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp

    Luo, X., Xue, Y., Xing, Z., Sun, J.: Prcbert: Prompt learning for requirement classification using bert-based pretrained language models. In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp. 1–13 (2022)

    2022

  80. [88]

    In: 2022 IEEE 30th International Requirements Engineering Conference (RE), pp

    Ajagbe, M., Zhao, L.: Retraining a bert model for transfer learning in requirements engineering: A preliminary study. In: 2022 IEEE 30th International Requirements Engineering Conference (RE), pp. 309–315 (2022). IEEE

    2022

  81. [89]

    In: Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, pp

    Kici, D., Bozanta, A., Cevik, M., Parikh, D., Ba¸ sar, A.: Text clas- sification on software requirements specifications using transformer models. In: Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, pp. 163–172 (2021)

    2021

  82. [90]

    In: Canadian Conference on AI, vol

    Kici, D., Malik, G., Cevik, M., Parikh, D., Basar, A.: A bert-based transfer learning approach to text classification on software requirements specifications. In: Canadian Conference on AI, vol. 1, p. 042077 (2021) Springer Nature 2021 LATEX template Generative Language Models 33

    2021

  83. [91]

    In: Proceedings of the 3rd ACM SIGSOFT Interna- tional Workshop on Machine Learning Techniques for Software Quality Evaluation, pp

    Rahman, M.A., Haque, M.A., Tawhid, M.N.A., Siddik, M.S.: Classify- ing non-functional requirements using rnn variants for quality software development. In: Proceedings of the 3rd ACM SIGSOFT Interna- tional Workshop on Machine Learning Techniques for Software Quality Evaluatio...

    2019

  84. [92]

    In: 2019 IEEE 43rd Annual Computer Software and Applica- tions Conference (COMPSAC), vol

    Baker, C., Deng, L., Chakraborty, S., Dehlinger, J.: Automatic multi- class non-functional software requirements classification using neural networks. In: 2019 IEEE 43rd Annual Computer Software and Applica- tions Conference (COMPSAC), vol. 2, pp. 610–615 (2019). IEEE

    2019

  85. [93]

    Advances in Science and Technology

    Kocerka, J., Krze´ slak, M., Ga luszka, A.: Ontology extraction from soft- ware requirements using named-entity recognition. Advances in Science and Technology. Research Journal 16(3) (2022)

    2022

  86. [94]

    In: 2019 IEEE 27th International Requirements Engineering Conference (RE), pp

    Pudlitz, F., Brokhausen, F., Vogelsang, A.: Extraction of system states from natural language requirements. In: 2019 IEEE 27th International Requirements Engineering Conference (RE), pp. 211–222 (2019). IEEE

    2019

  87. [95]

    ACM Computing Surveys 56(2), 1–40 (2023)

    Min, B., Ross, H., Sulem, E., Veyseh, A.P.B., Nguyen, T.H., Sainz, O., Agirre, E., Heintz, I., Roth, D.: Recent advances in natural lan- guage processing via large pre-trained language models: A survey. ACM Computing Surveys 56(2), 1–40 (2023)

    2023

  88. [96]

    ACM Transactions on Computing for Healthcare (HEALTH) 3(1), 1–23 (2021)

    Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., Poon, H.: Domain-specific language model pre- training for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH) 3(1), 1–23 (2021)

    2021

  89. [97]

    In: AIAA SCITECH 2023 Forum, p

    Tikayat Ray, A., Pinon-Fischer, O.J., Mavris, D.N., White, R.T., Cole, B.F.: aerobert-ner: Named-entity recognition for aerospace requirements engineering using bert. In: AIAA SCITECH 2023 Forum, p. 2583 (2023)

    2023

  90. [98]

    Journal of Information Processing 31, 143–153 (2023)

    Chow, M.Y.: Analysis of embedded system’s functional requirement using bert-based name entity recognition for extracting io entities. Journal of Information Processing 31, 143–153 (2023)

    2023

  91. [99]

    In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp

    Lopez, P., Du, C., Cohoon, J., Ram, K., Howison, J.: Mining software entities in scientific literature: document-level ner for an extremely imbal- ance and large-scale task. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 398...

    2021

  92. [100]

    arXiv preprint arXiv:2301.03709 (2023) Springer Nature 2021 LATEX template 34 Generative Language Models

    Malik, G., Yildirim, S., Cevik, M., Bener, A., Parikh, D.: Transfer learn- ing for conflict and duplicate detection in software requirement pairs. arXiv preprint arXiv:2301.03709 (2023) Springer Nature 2021 LATEX template 34 Generative Language Models

    2023 arXiv

  93. [101]

    In: Interna- tional Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), vol

    Zhou, Z., Wang, J., Niu, S.: Named entity recognition of military requirements document based on bert and global pointer. In: Interna- tional Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), vol. 12330, pp. 126–132 (2022). SPIE

    2022

  94. [102]

    arXiv preprint arXiv:2206.13690 (2022)

    Malik, G., Cevik, M., Parikh, D., Basar, A.: Identifying the requirement conflicts in srs documents using transformer-based sentence embeddings. arXiv preprint arXiv:2206.13690 (2022)

    2022 arXiv

  95. [103]

    In: The 35th Canadian Conference on Artificial Intelligence (2022)

    Malik, G., Cevik, M., Bera, S., Yildirim, S., Parikh, D., Basar, A.: Soft- ware requirement-specific entity extraction using transformer models. In: The 35th Canadian Conference on Artificial Intelligence (2022)

    2022

  96. [104]

    In: 2023 IEEE 31st International Requirements Engineering Conference (RE), pp

    Das, S., Deb, N., Cortesi, A., Chaki, N.: Zero-shot learning for named entity recognition in software specification documents. In: 2023 IEEE 31st International Requirements Engineering Conference (RE), pp. 100– 110 (2023). IEEE

    2023

  97. [105]

    Complex & Intelligent Systems 9(1), 25–39 (2023)

    Tang, M., Li, T., Gao, W., Xia, Y.: Attensy-sner: software knowledge entity extraction with syntactic features and semantic augmentation information. Complex & Intelligent Systems 9(1), 25–39 (2023)

    2023

  98. [106]

    Simon and Schuster, ??? (2023)

    Smart, J.F., Molak, J.: BDD in Action: Behavior-driven Development for the Whole Software Lifecycle. Simon and Schuster, ??? (2023)

    2023

  99. [107]

    International Journal of Creative Research Thoughts 11(8) (2023)

    Pothukuchi, A.S., Kota, L.V., Mallikarjunaradhya, V.: Impact of genera- tive ai on the software development lifecycle (sdlc). International Journal of Creative Research Thoughts 11(8) (2023)

    2023

  100. [108]

    Information and Software Technology 157, 107162 (2023)

    Sangaroonsilp, P., Dam, H.K., Choetkiertikul, M., Ragkhitwetsagul, C., Ghose, A.: A taxonomy for mining and classifying privacy requirements in issue reports. Information and Software Technology 157, 107162 (2023)

    2023

  101. [109]

    arXiv preprint arXiv:2311.13871 (2023)

    Abualhaija, S., Ceci, M., Briand, L.: Legal requirements analysis. arXiv preprint arXiv:2311.13871 (2023)

    2023 arXiv

  102. [110]

    Hashtroudi, S.P.: Automated test case generation using transformers and domain adaptation (2023)

    2023

  103. [111]

    Empirical Software Engineering 28(2), 36 (2023)

    Lukasczyk, S., Kroiß, F., Fraser, G.: An empirical study of automated unit test generation for python. Empirical Software Engineering 28(2), 36 (2023)

    2023

  104. [112]

    Lende, S.P., Raghuwanshi, M.: Question answering system on education acts using nlp techniques. In: 2016 World Conference on Futuris- tic Trends in Research and Innovation for Social Welfare (Startup Springer Nature 2021 LATEX template Generative Language Models 35 Conclave), ...

    2016

  105. [113]

    International Journal Of Research In Advance Technology In Engineering (IJRATE) 1, 11–14 (2013)

    Mervin, R.: An overview of question answering system. International Journal Of Research In Advance Technology In Engineering (IJRATE) 1, 11–14 (2013)

    2013

  106. [114]

    In: 2022 IEEE 30th International Requirements Engineering Conference (RE), pp

    Abualhaija, S., Arora, C., Sleimi, A., Briand, L.C.: Automated question answering for improved understanding of compliance requirements: A multi-document study. In: 2022 IEEE 30th International Requirements Engineering Conference (RE), pp. 39–50 (2022). IEEE

    2022

  107. [115]

    In: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp

    Abualhaija, S., Arora, C., Briand, L.C.: Coreqqa: a compliance require- ments understanding using question answering tool. In: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 1682–1686 (2022)

    2022

  108. [116]

    In: 2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE), pp

    do Nascimento Vale, L., de Almeida Maia, M.: Towards a question answering assistant for software development using a transformer-based language model. In: 2021 IEEE/ACM Third International Workshop on Bots in Software Engineering (BotSE), pp. 39–42 (2021). IEEE

    2021

  109. [117]

    arXiv preprint arXiv:2003.10555 (2020)

    Clark, K., Luong, M.-T., Le, Q.V., Manning, C.D.: Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)

    2020 arXiv

  110. [118]

    Sayyad Shirabad, J., Menzies, T.: PROMISE Software Engineering Repository (2005)

    2005

  111. [119]

    IEEE Transactions on Software Engineering 43(10), 918–945 (2016)

    Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F.: Automated extrac- tion and clustering of requirements glossary terms. IEEE Transactions on Software Engineering 43(10), 918–945 (2016)

    2016

  112. [120]

    In: 2019 2nd Interna- tional Conference on Communication, Computing and Digital Systems (C-CODE), pp

    Hassan, M.A., Saleem, S., Khan, M.Z., Khan, M.U.G.: Story based video retrieval using deep visual and textual information. In: 2019 2nd Interna- tional Conference on Communication, Computing and Digital Systems (C-CODE), pp. 166–171 (2019). IEEE

    2019

  113. [121]

    In: 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp

    Summra, S., Usman, M.G., Muhammad, A., et al.: Supervised neural network for offline forgery detection of handwritten signature. In: 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6 (2021). IEEE

    2021

  114. [122]

    In: Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pp

    Chen, A., Stanovsky, G., Singh, S., Gardner, M.: Evaluating question answering evaluation. In: Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pp. 119–124 (2019) Springer Nature 2021 LATEX template 36 Generative Language Models

    2019

  115. [123]

    arXiv preprint arXiv:2108.06130 (2021)

    Risch, J., M¨ oller, T., Gutsch, J., Pietsch, M.: Semantic answer similarity for evaluating question answering models. arXiv preprint arXiv:2108.06130 (2021)

    2021 arXiv

  116. [124]

    In: Proceedings of the 13th International Conference on Natural Language Generation, pp

    Akermi, I., Heinecke, J., Herledan, F.: Transformer based natural lan- guage generation for question-answering. In: Proceedings of the 13th International Conference on Natural Language Generation, pp. 349–359 (2020)

    2020

  117. [125]

    Computaci´ on y Sistemas25(1), 23–32 (2021)

    Butt, S., Ashraf, N., Siddiqui, M.H.F., Sidorov, G., Gelbukh, A.: Transformer-based extractive social media question answering on tweetqa. Computaci´ on y Sistemas25(1), 23–32 (2021)

    2021

  118. [126]

    Machine translation 23, 105–115 (2009)

    Lavie, A., Denkowski, M.J.: The meteor metric for automatic evaluation of machine translation. Machine translation 23, 105–115 (2009)

    2009

  119. [127]

    In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp

    Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)

    2002

  120. [128]

    Automated Software Engineering 30(2), 30 (2023)

    Alturayeif, N., Aljamaan, H., Hassine, J.: An automated approach to aspect-based sentiment analysis of apps reviews using machine and deep learning. Automated Software Engineering 30(2), 30 (2023)

    2023

  121. [129]

    arXiv preprint arXiv:1911.03705 (2019)

    Lin, B.Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., Ren, X.: Commongen: A constrained text generation challenge for generative commonsense reasoning. arXiv preprint arXiv:1911.03705 (2019)

    2019 arXiv

  122. [130]

    Advances in Neural Information Processing Systems 33, 5776–5788 (2020)

    Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., Zhou, M.: Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems 33, 5776–5788 (2020)

    2020

  123. [131]

    OpenAI blog 1(8), 9 (2019)

    Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)

    2019

  124. [132]

    arXiv preprint arXiv:2402.07023 (2024)

    Pal, A., Sankarasubbu, M.: Gemini goes to med school: Exploring the capabilities of multimodal large language models on medical challenge problems & hallucinations. arXiv preprint arXiv:2402.07023 (2024)

    2024 arXiv

  125. [133]

    arXiv preprint arXiv:2406.06295 (2024) Springer Nature 2021 LATEX template Generative Language Models 37

    Zhang, Y., Xu, X., Du, R., Liu, H., Dong, Y., Tan, Z.-H., Wang, W., Ma, Z.: Zero-shot audio captioning using soft and hard prompts. arXiv preprint arXiv:2406.06295 (2024) Springer Nature 2021 LATEX template Generative Language Models 37

    2024 arXiv

  126. [134]

    In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

    Wu, H., Shi, X.: Adversarial soft prompt tuning for cross-domain sen- timent analysis. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2438–2447 (2022)

    2022

  127. [135]

    arXiv preprint arXiv:2210.03493 (2022)

    Zhang, Z., Zhang, A., Li, M., Smola, A.: Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493 (2022)

    2022 arXiv

  128. [136]

    arXiv preprint arXiv:2112.08633 (2021)

    Rubin, O., Herzig, J., Berant, J.: Learning to retrieve prompts for in- context learning. arXiv preprint arXiv:2112.08633 (2021)

    2021 arXiv

  129. [137]

    In: International Conference on Machine Learning, pp

    Sun, T., Shao, Y., Qian, H., Huang, X., Qiu, X.: Black-box tuning for language-model-as-a-service. In: International Conference on Machine Learning, pp. 20841–20855 (2022). PMLR

    2022

  130. [138]

    arXiv preprint arXiv:2311.07064 (2023)

    Melamed, R., McCabe, L.H., Wakhare, T., Kim, Y., Huang, H.H., Boix-Adsera, E.: Propane: Prompt design as an inverse problem. arXiv preprint arXiv:2311.07064 (2023)

    2023 arXiv

  131. [139]

    AI Open (2023)

    Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., Tang, J.: Gpt understands, too. AI Open (2023)

    2023

  132. [140]

    arXiv preprint arXiv:2101.00190 (2021)

    Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190 (2021)

    2021 arXiv

  133. [141]

    arXiv preprint arXiv:2104.08691 (2021)

    Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter- efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)

    2021 arXiv

  134. [142]

    arXiv preprint arXiv:2110.07602 (2021)

    Liu, X., Ji, K., Fu, Y., Tam, W.L., Du, Z., Yang, Z., Tang, J.: P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint arXiv:2110.07602 (2021)

    2021 arXiv

  135. [2009]

    386–400 (2009)

    Proceedings 10, pp. 386–400 (2009). Springer

    2009

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.