Pith. sign in

REVIEW 4 major objections 6 minor 49 references

IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A fine-tuned chatbot that turns everyday questions into academic search terms.

desk verdict A sincere system demo with an interesting LLM application, but the evaluation is circular and the probabilistic filter is underspecified, so the precision claims don't hold. read the letter →

arxiv 2412.07213 v1 pith:VXT76NYR submitted 2024-12-10 cs.IR cs.AI

Weizhen Bian , Siyan Liu , Yubo Zhou , Dezhi Chen , Yijie Liao , Zhenzhen Fan , Aobo Wang This is my paper · ORCID
classification cs.IRcs.AI
keywords literaturemanagementlargelanguagemodelfine-tuningGPT-3.5-turboprobabilisticpersonalizedsearchtermmatchingrecommendationsystem
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 introduces IntellectSeeker, a literature management platform that combines a probabilistic data-scraping filter with a fine-tuned GPT-3.5-turbo chatbot. The central claim is that this combination closes the gap between casual language and academic terminology, improving search precision and personalization. The authors report that their fine-tuned model outperforms several open-source and prompt-based baselines on translation-style metrics (BLEU 0.9269, ROUGE-1 0.9413, ROUGE-L 0.9355, METEOR 0.9553). If the approach holds, it would give newcomers a more forgiving way to find relevant literature without knowing field-specific vocabulary.

What carries the argument

The probabilistic model is a weighted similarity formula $I = w_p \cdot S(K_a, K_u) + w_i \cdot S(K_a, K_i)$, where $K_a$ are the article's features, $K_u$ the user's preference features, $K_i$ the manually inputted requirements, $w_p$ and $w_i$ weights, and $S$ the similarity function measuring overlap between keyword sets. All features are converted into codes before similarity is computed. This formula decides whether an article is added to the user's database, and the weights and threshold (0.75) are user-adjustable. The language model component is a fine-tuned GPT-3.5-turbo that translates everyday expressions into academic terms using a manually constructed corpus with a 9:1 train-validation split.

What would settle it

Run the full pipeline on a fixed set of 100 genuine literature queries from novices and measure retrieval precision against a manually curated gold standard. If the fine-tuned chatbot's term conversions do not improve hit rate over a plain keyword search, the central claim fails. Also, check the reported BLEU on an external dataset not generated with the same ChatGPT prompts.

Watch

Extended reading notes

Core claim

The paper claims that IntellectSeeker provides 'exceptional search precision and matching capabilities' by combining a probabilistic article-selection model with a fine-tuned large language model. The probabilistic model scores candidate articles by a weighted similarity between article features and both user preferences and explicit requirements, keeping articles above a 0.75 threshold (with a small random acceptance probability) to build a personalized database. The language model component, fine-tuned GPT-3.5-turbo on a corpus of 0.266k question-answer pairs, converts everyday language into academic terms, and the paper reports that this fine-tuned model 'outperforms others in all metrics' on BLEU, ROUGE-1, ROUGE-2, ROUGE-L, and METEOR in Table 1.

Load-bearing premise

The personalized-filtering claim depends on an unspecified similarity function $S$ that converts article and user features into codes and measures their overlap.

Editorial extensions

If this is right

  • If the fine-tuned model reliably converts casual queries into academic terms, literature search becomes easier for novices and non-native speakers.
  • The probabilistic filter could make each user's database gradually more personalized with use, since implicit preferences accumulate from clicks and bookmarks.
  • The reporting of translation-style metrics (BLEU, ROUGE, METEOR) suggests the authors regard term conversion as a machine-translation task, which implies that better conversion quality should translate directly into better search recall.
  • If the threshold-and-random-acceptance design works, it would create a balance between strictly relevant results and serendipitous discoveries.

Reading between the lines

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

  • The paper's central comparison rests on a single validation set; the 0.92+ scores could reflect the shape of that set rather than generalizable performance. A sensible test would be to evaluate the fine-tuned model on an external academic-query dataset not generated by the same ChatGPT prompts.
  • The probabilistic filter's similarity function $S$ is not fully specified. If $S$ is merely keyword overlap on 'codes,' it may ignore semantics and synonymy, limiting the claimed personalization to exact-match scenarios.
  • The design implies a workflow where users see one-line summaries and word clouds; a controlled user study could determine whether these visualizations actually speed up relevance judgment.
  • The claimed 'exceptional search precision' is asserted rather than measured; a direct comparison with Google Scholar or Semantic Scholar on a fixed query set would settle this more convincingly.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper presents IntellectSeeker, a literature management platform that combines a probabilistic data-scraping filter with a fine-tuned GPT-3.5-turbo model. The probabilistic filter computes an importance score I = wp*S(Ka,Ku) + wi*S(Ka,Ki) to decide whether an article enters the user's personalized database, with a fixed threshold of 0.75 and a random inclusion probability P=0.05. The language-model component is fine-tuned to convert everyday language into academic terminology, and Table 1 reports near-perfect BLEU/ROUGE/METEOR scores for the fine-tuned GPT-3.5-turbo. The paper also describes recommendation, summarization, and word-cloud features, and it claims 'exceptional search precision and matching capabilities.' The core quantitative evidence for this claim is the LLM evaluation in Section 4.1, together with the equation-based probabilistic filter in Section 3.2.

Significance. If rigorously validated, the idea of an LLM-based query-to-academic-terminology conversion and a behavior-driven probabilistic filter would be useful for novice researchers. The paper also ships a system description and a GitHub link, which is a positive step for reproducibility. However, the current manuscript does not provide credible evidence for its central claims: the similarity function S in Eq. (1) is never defined, the filtering thresholds are asserted without calibration, and the LLM evaluation is circular because both training and validation data are generated by ChatGPT using the same manually defined output format. As a result, the reported high metrics mostly demonstrate format and template reproduction rather than improved search precision, and the paper's core personalization mechanism is not reproducible or testable.

major comments (4)
  1. [Section 3.2, Eq. (1)] The similarity function S is never specified. The text states only that 'all features K will be converted into codes, then the similarity will be calculated,' but no encoding scheme, distance measure, or implementation detail is given for S, nor is the feature representation of Ka, Ku, or Ki defined. Since Eq. (1) is the entire formal basis for the personalized filtering claim, this omission makes the model untestable and non-reproducible. A reader cannot compute I, cannot verify the threshold behavior, and cannot reimplement the system without guessing the semantics of S.
  2. [Section 4.1, Table 1] The validation set is not independent human ground truth. The authors write that they 'manually wrote several prompts' and then used those prompts 'to guide ChatGPT in generating more question-and-answer pairs in the same format defined manually.' The resulting dataset is split 9:1 into training and validation sets, so both sets come from the same ChatGPT generation process and the same fixed output format. The BLEU, ROUGE, and METEOR scores in Table 1 therefore largely measure whether the fine-tuned model reproduces the prompt template and formatting conventions, not whether it improves real-world search precision. The abstract's claim of 'exceptional search precision and matching capabilities' needs support from an independent human-annotated evaluation or a retrieval experiment with real queries and relevance judgments.
  3. [Section 3.2] The inclusion threshold of 0.75 and the random inclusion probability of P=0.05 are stated without any calibration, sensitivity analysis, or justification. Because the distribution of the importance score I is not characterized and S is undefined, these numerical choices cannot be interpreted or evaluated. The paper provides no evidence about how varying these parameters affects precision, recall, or database personalization, despite these parameters being load-bearing for the filtering mechanism.
  4. [Sections 3.3, 3.4, and 4] No end-to-end evaluation of the complete IntellectSeeker system is provided. The only reported quantitative results are component-level LLM text-conversion metrics on the synthetic validation set. There are no retrieval precision/recall measurements, no user study, no comparison against Google Scholar or other literature management tools, and no evaluation of the recommendation or probabilistic-filtering components. Consequently, the central claim that IntellectSeeker provides a 'highly customizable literature management solution with exceptional search precision' is unsupported at the system level.
minor comments (6)
  1. [Keywords] The keyword 'few-short learning' should be 'few-shot learning.'
  2. [Section 4.1 heading] The heading contains a stray space: 'T rain Semantic Enhancement Model' should be 'Train Semantic Enhancement Model.'
  3. [Reference [19]] The model name is misspelled as 'LaMDAMDA'; it should be 'LaMDA.'
  4. [Section 4.1] The sentence 'For the common unrelated questions, we allowed ChatGPT to generate them randomly' is unclear; it should be reworded, for example as 'For unrelated common questions, we allowed ChatGPT to generate responses randomly.'
  5. [Section 4.1 and Table 1] The text mentions 'Gemma and Mixtral with the highest MT-bench score,' but Table 1 lists Gemma2-2B and Mistral-7B rather than Mixtral; please reconcile the model names.
  6. [Section 4.1] The corpus size is given only as '0.266k rows'; please report the exact numbers of training and validation examples so readers can assess the scale of the fine-tuning experiment.

Circularity Check

1 steps flagged · score 6.0 of 10

The LLM component's quantitative claim of 'exceptional search precision and matching capabilities' rests on BLEU/ROUGE/METEOR scores computed against a ChatGPT-generated validation set created with the same manually defined output format and same data-generation procedure as the training set; the benchmark measures self-consistency with the generator rather than independent search-precision…

  1. fitted input called prediction [Section 4.1, 'Train Semantic Enhancement Model' (Data Corpus Construction; Fine-tuning and Iterative Learning), Table 1]
    "These manually written prompts were then used to guide ChatGPT in generating more question-and-answer pairs in the same format defined manually. ... In fine-tuning the models, the dataset was split into 9:1 as a training set and validation set. ... The results demonstrate that the fine-tuned GPT-3.5-turbo model outperforms others in all metrics, achieving a BLEU score of 0.9269"

    The validation reference for the fine-tuned model is not independent human ground truth: it is held-out output of ChatGPT, produced under the same manually defined response format and the same prompt-generation procedure as the training data. The model is fitted to reproduce that distribution, and the reported BLEU/ROUGE/METEOR scores then measure agreement with the same generator's format. The headline 'outperforms others in all metrics' is therefore a measure of self-consistency with the data-generation pipeline, not evidence of real-world mapping from everyday language to verified academic vocabulary or of search precision. The prediction is statistically tied to the fitted input by construction.

full rationale

The only load-bearing quantitative evidence for the paper's claimed 'exceptional search precision and matching capabilities' is Table 1, and that evidence is circular as described above: the fine-tuned GPT-3.5-turbo is trained and evaluated on ChatGPT-generated question-answer pairs using the same manually defined format, so the near-perfect overlap metrics largely certify format reproduction. No external benchmark, user study, or retrieval-quality evaluation is provided. The probabilistic data-scraping model in Eq. (1) is under-specified—the similarity function S is never defined beyond 'all features K will be converted into codes, then the similarity will be calculated'—but this is a reproducibility gap, not a circularity. There is no load-bearing self-citation chain: the references are external prior work, and no 'uniqueness theorem' is invoked. Because the training/validation split is real and the model could in principle have failed to match the held-out ChatGPT outputs, the circularity is partial rather than definitionally forced, hence a score of 6 rather than higher.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

No new physical or formal entities are introduced. The system relies on two ad hoc modeling choices: an undefined similarity function and a self-generated evaluation set, plus hand-set thresholds and weights.

free parameters (4)
  • inclusion threshold = 0.75
    Chosen by hand in Section 3.2; articles with importance above 0.75 are saved.
  • random inclusion probability P = 0.05
    Set in Section 3.2 so below-threshold articles are occasionally collected.
  • weights wp and wi
    User-configurable weights for preference versus manual input in Eq. (1); no values or calibration are given.
  • word cloud term count = 20
    The top 20 most frequent terms are used for the word cloud in Section 3.4.
assumptions (3)
  • ad hoc to paper The similarity function S in Eq. (1) measures overlap between sets of keywords.
    Invoked in Section 3.2 but never defined: features are 'converted into codes' with no encoding or distance specified.
  • domain assumption The ChatGPT-generated corpus with a manually defined output format is a valid measure of everyday-to-academic translation quality.
    The validation set is generated from the same prompt templates as the training set in Section 4.1, so near-perfect scores may reflect format memorization rather than generalization.
  • domain assumption A weighted linear combination of keyword-overlap scores captures user relevance.
    Eq. (1) assumes relevance decomposes into user-preference and manual-input terms with fixed constants 0.75 and 0.05; no evidence is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model." pith.science (2026). https://pith.science/paper/VXT76NYR

@misc{pith2026241207213,
  author       = {Pith},
  title        = {Pith review of: IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VXT76NYR}},
  note         = {Machine review of arXiv:2412.07213}
}
read the original abstract

Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challenges. This platform integrates a Large Language Model (LLM)--based semantic enhancement bot with a sophisticated probability model to personalize and streamline literature searches. We adopted the GPT-3.5-turbo model to transform everyday language into professional academic terms across various scenarios using multiple rounds of few-shot learning. This adaptation mainly benefits academic newcomers, effectively bridging the gap between general inquiries and academic terminology. The probabilistic model intelligently filters academic articles to align closely with the specific interests of users, which are derived from explicit needs and behavioral patterns. Moreover, IntellectSeeker incorporates an advanced recommendation system and text compression tools. These features enable intelligent article recommendations based on user interactions and present search results through concise one-line summaries and innovative word cloud visualizations, significantly enhancing research efficiency and user experience. IntellectSeeker offers academic researchers a highly customizable literature management solution with exceptional search precision and matching capabilities. The code can be found here: https://github.com/LuckyBian/ISY5001

Figures

Figures reproduced from arXiv: 2412.07213 by the authors.

Figure 1
Figure 1. Overview of IntellectSeeker: IntellectSeeker features three core components: Probabilistic Data Scraping, Enhanced Search Engine, and Interactive Data Explo￾ration and Personalized Recommendation. It uses probabilistic algorithms to filter web data that are closely aligned with user preferences. The search engine dynamically ad￾justs search results based on user queries and preferences, incorporating popular and per… view at source ↗
Figure 2
Figure 2. IntellectSeeker’s data crawling process: This diagram illustrates Intellect￾Seeker’s data crawling process, divided into three key stages. First, user input guides the system in extracting relevant data from the target website using a probabilistic model. Subsequently, the extracted data is evaluated against predefined thresholds to determine its relevance. Finally, it is stored through pointers to ensure storage sp… view at source ↗
Figure 3
Figure 3. Overview of LLM Search Engine: it outlines a Large Language Model’s training and deployment workflow. During the ’Training Phase,’ a new corpus is created and used to fine-tune a pre-trained large language model. Once validated, the model is de￾ployed to handle user queries, recognizing and processing inquiries into various formats. Key components include the ’Judgment Box’ for response evaluation, ’Actions’ guiding… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

49 extracted references · 35 canonical work pages

  1. [1]

    An anatomization of research paper recommender system: Overview, approaches and challenges

    Ritu Sharma, Dinesh Gopalani, and Yogesh Meena. An anatomization of research paper recommender system: Overview, approaches and challenges. Engineering Applications of Artificial Intelligence, 118:105641, 2023

    work page 2023

  2. [2]

    Approximate data collection in sensor networks using probabilistic models

    David Chu, Amol Deshpande, Joseph M Hellerstein, and Wei Hong. Approximate data collection in sensor networks using probabilistic models. In22nd International Conference on Data Engineering (ICDE’06), pages 48–48. IEEE, 2006

    work page 2006

  3. [3]

    Gali Halevi, Henk Moed, and Judit Bar-Ilan. Suitability of google scholar as a source of scientific information and as a source of data for scientific evalua- tion—review of the literature.Journal of informetrics, 11(3):823–834, 2017

    work page 2017

  4. [4]

    Joonseok Lee, Kisung Lee, and Jennifer G. Kim. Personalized academic research paper recommendation system, 2013

    work page 2013

  5. [5]

    Scholarly paper recommendation via user’s recent research interests

    Kazunari Sugiyama and Min-Yen Kan. Scholarly paper recommendation via user’s recent research interests. In Proceedings of the 10th annual joint conference on Digital libraries, pages 29–38, 2010

    work page 2010

  6. [6]

    Research articles suggestion using topic modelling

    Venkat Chaitanya and Pramod Kumar Singh. Research articles suggestion using topic modelling. In 2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI), pages 178–182. IEEE, 2017

    work page 2017

  7. [7]

    Citation recommendation using semantic repre- sentation of cited papers’ relations and content.Expert systems with applications, 187:115826, 2022

    Jinzhu Zhang and Lipeng Zhu. Citation recommendation using semantic repre- sentation of cited papers’ relations and content.Expert systems with applications, 187:115826, 2022

    work page 2022

  8. [8]

    Cares: a ranking-orientedcadalrecommendersystem

    Chenxing Yang, Baogang Wei, Jiangqin Wu, Yin Zhang, and Liang Zhang. Cares: a ranking-orientedcadalrecommendersystem. In Proceedings of the 9th ACM/IEEE- CS joint conference on Digital libraries, pages 203–212, 2009

    work page 2009

Show all 49 references
  1. [9]

    Collaborative filtering-based recommender system: Approaches and research challenges

    Ritu Sharma, Dinesh Gopalani, and Yogesh Meena. Collaborative filtering-based recommender system: Approaches and research challenges. In 2017 3rd inter- national conference on computational intelligence & communication technology (CICT), pages 1–6. IEEE, 2017

    2017

  2. [10]

    A pagerank-based collabo- rative filtering recommendation approach in digital libraries

    Shanshan Guo, Wenyu Zhang, and Shuai Zhang. A pagerank-based collabo- rative filtering recommendation approach in digital libraries. Tehnički vjesnik, 24(4):1051–1058, 2017

    2017

  3. [11]

    Trust-based recommendations for scien- tific papers based on the researcher’s current interest

    Shaikhah Alotaibi and Julita Vassileva. Trust-based recommendations for scien- tific papers based on the researcher’s current interest. InArtificial Intelligence in Education: 16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013. Proceedings 16, pages 71...

    2013

  4. [12]

    Learning multiple graphs for document recommendations

    Ding Zhou, Shenghuo Zhu, Kai Yu, Xiaodan Song, Belle L Tseng, Hongyuan Zha, and C Lee Giles. Learning multiple graphs for document recommendations. In Proceedings of the 17th international conference on World Wide Web, pages 141– 150, 2008

    2008

  5. [13]

    Publication venue recommendation based on paper’s title and co-authors network

    Ramin Safa, SeyedAbolghasem Mirroshandel, Soroush Javadi, and Mohammad Azizi. Publication venue recommendation based on paper’s title and co-authors network. Information Systems & Telecommunication, page 33, 2018

    2018

  6. [14]

    Personalized scientific paper recommendation based on heterogeneous graph representation.IEEE Access, 7:79887–79894, 2019

    Xiao Ma and Ranran Wang. Personalized scientific paper recommendation based on heterogeneous graph representation.IEEE Access, 7:79887–79894, 2019

    2019

  7. [15]

    Ammus: A survey of transformer-based pretrained models in natural language processing

    Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, and Sivanesan Sangeetha. Ammus: A survey of transformer-based pretrained models in natural language processing. arXiv preprint arXiv:2108.05542, 2021

    2021 arXiv

  8. [16]

    Bian et al

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Pra- fulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 12 W. Bian et al. et al. Language models are few-shot learners.Advances in neural information pro- cessing systems, 33:18...

    1901

  9. [17]

    Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

    Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebas- tian Gehrmann, et al. Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

    2023

  10. [18]

    Palm 2 technical report.arXiv preprint arXiv:2305.10403, 2023

    Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. Palm 2 technical report.arXiv preprint arXiv:2305.10403, 2023

    2023 arXiv

  11. [19]

    Lamda: Language models for dialog applications.arXiv preprint arXiv:2201.08239, 2022

    Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kul- shreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. Lamda: Language models for dialog applications.arXiv preprint arXiv:2201.08239, 2022

    2022 arXiv

  12. [20]

    Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model.arXiv preprint arXiv:2201.11990, 2022

    Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Ra- jbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al. Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model...

    2022 arXiv

  13. [21]

    Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  14. [22]

    Gpt-4 technical report.arXiv, pages 2303–08774, 2023

    R OpenAI. Gpt-4 technical report.arXiv, pages 2303–08774, 2023

    2023

  15. [23]

    Bloomberggpt: A large language model for finance

    Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Se- bastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564, 2023

    2023 arXiv

  16. [24]

    Codegen2: Lessons for training llms on programming and natural languages.arXiv preprint arXiv:2305.02309, 2023

    Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou. Codegen2: Lessons for training llms on programming and natural languages.arXiv preprint arXiv:2305.02309, 2023

    2023 arXiv

  17. [25]

    Large language models encode clinical knowledge

    Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. Large language models encode clinical knowledge. Nature, 620(7972):172–180, 2023

    2023

  18. [26]

    Goat: Fine-tuned llama outperforms gpt-4 on arithmetic tasks.arXiv preprint arXiv:2305.14201, 2023

    Tiedong Liu and Bryan Kian Hsiang Low. Goat: Fine-tuned llama outperforms gpt-4 on arithmetic tasks.arXiv preprint arXiv:2305.14201, 2023

    2023 arXiv

  19. [27]

    Visconde: Multi-document qa with gpt-3 and neural reranking

    Jayr Pereira, Robson Fidalgo, Roberto Lotufo, and Rodrigo Nogueira. Visconde: Multi-document qa with gpt-3 and neural reranking. InEuropean Conference on Information Retrieval, pages 534–543. Springer, 2023

    2023

  20. [28]

    A comprehensive capability analysis of gpt-3 and gpt-3.5 series models.arXiv preprint arXiv:2303.10420, 2023

    Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui, Zeyang Zhou, Chao Gong, Yang Shen, et al. A comprehensive capability analysis of gpt-3 and gpt-3.5 series models.arXiv preprint arXiv:2303.10420, 2023

    2023 arXiv

  21. [29]

    Towards zero-shot and few-shot table question answering using gpt-3.arXiv preprint arXiv:2210.17284, 2022

    Pragya Srivastava, Tanuja Ganu, and Saikat Guha. Towards zero-shot and few-shot table question answering using gpt-3.arXiv preprint arXiv:2210.17284, 2022

    2022 arXiv

  22. [30]

    Discovering user preferences by using time entries in click-through data to improve search engine results

    Parthasarathy Ramachandran. Discovering user preferences by using time entries in click-through data to improve search engine results. InDiscovery Science: 8th International Conference, DS 2005, Singapore, October 8–11, 2005. Proceedings 8, pages 383–385. Springer, 2005

    2005

  23. [31]

    Can chat- gpt understand too? a comparative study on chatgpt and fine-tuned bert.arXiv preprint arXiv:2302.10198, 2023

    Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. Can chat- gpt understand too? a comparative study on chatgpt and fine-tuned bert.arXiv preprint arXiv:2302.10198, 2023. 13

    2023 arXiv

  24. [32]

    Context preserving dynamic word cloud visualization

    Weiwei Cui, Yingcai Wu, Shixia Liu, Furu Wei, Michelle X Zhou, and Huamin Qu. Context preserving dynamic word cloud visualization. In2010 IEEE Pacific Visualization Symposium (PacificVis), pages 121–128. IEEE, 2010

    2010

  25. [33]

    Data require- ments for evaluation of personalization of information retrieval-a position paper

    Nicholas J Belkin, Daniel Hienert, Philipp Mayr, and Chirag Shah. Data require- ments for evaluation of personalization of information retrieval-a position paper. arXiv preprint arXiv:1809.02412, 2018

    2018 arXiv

  26. [34]

    Database management as a service: Challenges and opportunities

    Divyakant Agrawal, Amr El Abbadi, Fatih Emekci, and Ahmed Metwally. Database management as a service: Challenges and opportunities. In2009 IEEE 25th international conference on data engineering, pages 1709–1716. IEEE, 2009

    2009

  27. [35]

    Michael Gusenbauer and Neal R Haddaway. Which academic search systems are suitable for systematic reviews or meta-analyses? evaluating retrieval qualities of google scholar, pubmed, and 26 other resources.Research synthesis methods, 11(2):181–217, 2020

    2020

  28. [36]

    McKenzie, Rodney Harries, and Timothy Bell

    Bruce J. McKenzie, Rodney Harries, and Timothy Bell. Selecting a hashing algo- rithm. Software: Practice and Experience, 20(2):209–224, 1990

    1990

  29. [37]

    Decision tree methods: applications for classification and prediction

    Yan-Yan Song and LU Ying. Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2):130, 2015

    2015

  30. [38]

    Gemma: Open models based on gemini research and technology.arXiv preprint arXiv:2403.08295, 2024

    Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupati- raju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. Gemma: Open models based on gemini research and technology.arXiv preprint arXiv:2403.08295, 2024

    2024 arXiv

  31. [39]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Deven- dra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guil- laume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Tho...

    2023

  32. [40]

    Train- ing language models to follow instructions with human feedback

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Train- ing language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27...

    2022

  33. [41]

    Smmry api

    SMMRY. Smmry api. https://smmry.com/api, 2024

    2024

  34. [42]

    User-based collaborative-filtering recom- mendation algorithms on hadoop

    Zhi-Dan Zhao and Ming-Sheng Shang. User-based collaborative-filtering recom- mendation algorithms on hadoop. In2010 third international conference on knowl- edge discovery and data mining, pages 478–481. IEEE, 2010

    2010

  35. [43]

    Item-based top-n recommendation algo- rithms

    Mukund Deshpande and George Karypis. Item-based top-n recommendation algo- rithms. ACM Transactions on Information Systems (TOIS), 22(1):143–177, 2004

    2004

  36. [44]

    Item popularity and recommendation accuracy

    Harald Steck. Item popularity and recommendation accuracy. InProceedings of the fifth ACM conference on Recommender systems, pages 125–132, 2011

    2011

  37. [45]

    A hybrid recommendation approach for a tourism system.Expert systems with applications, 40(9):3532–3550, 2013

    Joel P Lucas, Nuno Luz, María N Moreno, Ricardo Anacleto, Ana Almeida Figueiredo, and Constantino Martins. A hybrid recommendation approach for a tourism system.Expert systems with applications, 40(9):3532–3550, 2013

    2013

  38. [46]

    A new academic word list.TESOL quarterly, 34(2):213–238, 2000

    Averil Coxhead. A new academic word list.TESOL quarterly, 34(2):213–238, 2000

    2000

  39. [47]

    Framework for testing multi-threaded java programs.Concurrency and Com- putation: Practice and Experience, 15(3-5):485–499, 2003

    Orit Edelstein, Eitan Farchi, Evgeny Goldin, Yarden Nir, Gil Ratsaby, and Shmuel Ur. Framework for testing multi-threaded java programs.Concurrency and Com- putation: Practice and Experience, 15(3-5):485–499, 2003

    2003

  40. [48]

    Ayisha Tabassum and Rajendra R Patil. A survey on text pre-processing & fea- ture extraction techniques in natural language processing.International Research Journal of Engineering and Technology (IRJET), 7(06):4864–4867, 2020

    2020

  41. [49]

    Making pointer-based data structures cache conscious.Computer, 33(12):67–74, 2000

    Trishul M Chilimbi, Mark D Hill, and James R Larus. Making pointer-based data structures cache conscious.Computer, 33(12):67–74, 2000

    2000

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.