Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study

Anneketh Vij; Ayan Bhowmick; Changhao Liu; Edward Shi; Rahul Anil Nair; Theodore Eugene Ho

REVIEW 5 major objections 5 minor 1 cited by

Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study

T0 review · 5 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read This study reports that fine-tuning the larger model Phi-2 degraded ingredient coverage and temperature/time accuracy in generated recipes, while smaller SmolLM models held their own.

desk verdict A well-intentioned recipe-generation benchmark whose load-bearing custom metrics are unvalidated; results are suggestive, not conclusive. read the letter →

arxiv 2502.02028 v2 pith:AXPVJ57A submitted 2025-02-04 cs.CL cs.AI

Anneketh Vij , Changhao Liu , Rahul Anil Nair , Theodore Eugene Ho , Edward Shi , Ayan Bhowmick This is my paper · ORCID

classification cs.CLcs.AI

keywords recipegenerationfine-tuninglanguagemodelsdomain-specificevaluationmetricsingredientcoverageallergensubstitutionretrieval-augmentedLLM-as-a-judge

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 tries to establish that standard fine-tuning is not a dependable route to better domain-specific recipe generation: for Phi-2, the largest model tested, fine-tuning cut ingredient coverage from 0.59 to 0.30 and temperature/time specification from 0.329 to 0.24, while SmolLM-360M and SmolLM-1.7B performed comparably despite their size difference. The paper argues that traditional metrics such as BLEU and ROUGE cannot reveal this because they reward overlap with a single ground-truth recipe and punish creative divergence. It therefore builds a multi-dimensional evaluation with recipe-specific scores for ingredient coverage, step complexity, coherence, and temperature/time checks, plus an LLM-based judge, and applies it to baseline and fine-tuned models with and without allergen substitution. If the framework holds, evaluation of creative generation tasks should include domain-specific quality signals rather than relying on overlap metrics alone.

What carries the argument

The load-bearing machinery is the paper's four recipe-specific auto-evaluation metrics: ingredient coverage (does the generated text use the listed ingredients?), step complexity (how detailed and parameterized are the instructions?), recipe coherence (does the step dependency graph make logical and temporal sense?), and temperature/time specification (are cooking parameters present and in plausible ranges?). These scores, not the overlap metrics, are what generate the paper's central contrast between Phi-2's degradation and SmolLM's comparability. A supplementary LLM-as-a-judge rubric covering clarity, completeness, consistency, practicality, relevance, and allergen safety provides a second lens on the same generated recipes.

What would settle it

Have a panel of human cooks rate a blinded sample of the baseline and fine-tuned recipes and compare their ratings with the four domain-specific scores; if human rankings do not reproduce the reported Phi-2 degradation and SmolLM comparability, the central claim is not supported.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that fine-tuning changes what a recipe model does rather than simply improving it. Phi-2's fine-tuned version scored higher on step complexity (from 0.79 to 0.99) but lower on ingredient coverage (from 0.59 to 0.30), recipe coherence (from 0.08 to 0.07), and temperature/time specification (from 0.329 to 0.24), which the authors interpret as a trade-off between producing complete step-by-step instructions and preserving the semantic relations among ingredients. The two SmolLM sizes behaved similarly to each other both before and after fine-tuning, suggesting that parameter count is not the main driver of recipe quality. The paper also reports that prompt-based and retrieval-based allergen substitution both lower some quality scores, and that the multi-dimensional evaluation exposes discrepancies that BLEU and ROUGE scores hide.

Load-bearing premise

The central claims stand on the assumption that the paper's hand-built recipe-quality scores, especially step complexity and recipe coherence, really measure culinary quality, but those scores are never calibrated against human judgment.

Editorial extensions

If this is right

Fine-tuning on domain text can improve surface structure, such as step-by-step formatting, while eroding fidelity to the input ingredients and cooking parameters; recipe systems should track both dimensions.
Model scale alone does not determine post-fine-tuning recipe quality: the SmolLM-1.7B and SmolLM-360M models landed close together, so smaller, cheaper models can be a sensible choice for this task.
Overlap-based metrics like BLEU and ROUGE should not be the primary yardstick for creative generation; the paper's domain-specific scores and LLM judge give a different, more practical picture.
Allergen substitution, whether prompt-driven or retrieval-driven, changes the quality profile of generated recipes; substitution is not a free add-on and needs its own evaluation.
The step-complexity versus coherence trade-off suggests that conventional fine-tuning objectives may need to be rethought for specialized domains where semantic correctness matters as much as fluency.

Reading between the lines

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

A direct human-rating study on the same 500 test recipes would settle whether the domain-specific scores track actual culinary quality; if human rankings do not reproduce the reported Phi-2 degradation and SmolLM comparability, the benchmark would need revision before the central finding could be trusted.
The same metric structure, coverage of input items, step dependency, and parameter specification, could transfer to other structured instruction-generation tasks such as workout plans, medication instructions, or DIY repair guides, where missing a detail is costly.
The Phi-2 pattern suggests that standard language-model fine-tuning may teach a model to emit recipe-like scaffolding, numbered steps, temperatures, and times, while weakening its link to the specific ingredient list; a testable extension is whether instruction-tuning or reinforcement-learning objectives recover ingredient coverage without sacrificing step detail.
The fact that retrieval-based substitution lowered ingredient coverage while improving step complexity implies that post-hoc substitution fixes allergens but not the model's underlying planning; an editing pass with faithfulness constraints on the final ingredient list would be a natural next system to test.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

This paper does a respectable sweep: it fine-tunes several small LMs (T5, SmolLM, Phi-2) on Food.com and evaluates with standard plus recipe-specific metrics, plus prompt-based and RAG allergen substitution. That combination is the real contribution, and the paper is transparent about its compute constraints and sample size.

The central finding—Phi-2's ingredient coverage and temp/time scores drop after fine-tuning while step complexity rises—is interesting if the metrics are trustworthy. But they aren't, not yet. The four custom metrics in §4.2/Appendix E are described as bullet-point heuristics: count distinct operations, build dependency graph, validate ranges. No formulas, no thresholds, no released implementation, no calibration against human or expert judgment. Without that, the low coherence scores (0.02–0.12 for everything) look like the metric is capturing formatting, not culinary sense. The appendix samples show fine-tuned Phi-2 output that is visibly worse, yet gets coherence scores comparable to baselines. If the metric can't distinguish what a human sees, the fine-tuning degradation claim is on shaky ground.

Also, all comparisons are point estimates with no error bars or significance tests. The limitations section mentions the 500-sample evaluation and that LLM-as-a-judge is stochastic, but doesn't address the missing validation of the auto-metrics, which is the load-bearing piece.

None of this is fatal to the paper as an exploratory study. The allergen substitution systems and the idea of a multi-dimensional recipe evaluation are worth thinking about. The authors are honest about the exploratory nature. But the abstract and discussion frame the results as if the framework is already reliable, and that's an overreach.

For a serious venue, I'd send it to review only with the expectation of major revisions: validate the metrics against human judgments, release code and data, add error bars or significance tests, and temper the conclusions. As is, it's more of a workshop paper or a useful starting point for the authors' future work. I wouldn't cite it yet, but I'd point students to it as an example of domain-specific evaluation design.

Referee Report

5 major / 5 minor

Summary. The paper fine-tunes several small language models—T5-small, GPT-2 (small/medium), SmolLM-135M/360M/1.7B, and Phi-2—on the Food.com recipe dataset for the task of generating cooking instructions from recipe names and ingredient lists. It evaluates the resulting generations with traditional metrics (BLEU, ROUGE, perplexity), four newly proposed domain-specific auto-metrics (ingredient coverage, step complexity, recipe coherence, temperature/time specification), and a Qwen2.5-7B LLM-as-a-judge. It also develops prompt-based and RAG-assisted allergen substitution systems. The main conclusions are that fine-tuning Phi-2 degrades its domain-specific scores (ingredient coverage dropping from 0.59 to 0.30 and temperature/time from 0.329 to 0.24 in Table 3), that SmolLM-360M and SmolLM-1.7B perform comparably despite the size difference, and that the multi-dimensional evaluation framework reveals limitations of traditional overlap-based metrics for creative generation.

Significance. The paper addresses a relevant problem—domain-specific evaluation for creative NLG—and its broad comparison across model scales and architectures is useful empirical groundwork. The appendices are reasonably detailed, including hyperparameters (J–M), sample generations (B, D, G), and an allergen substitution database (F). However, the central findings rest entirely on four custom metrics whose operational definitions are not provided (Section 4.2 and Appendix E), and which are not validated against human judgment. Some reported scores are inconsistent with the paper's own sample outputs (e.g., fine-tuned SmolLM-360M in Table 10 vs. Table 3). Because of this, the significance is currently conditional: if the metrics were made precise, released, and calibrated against human ratings, the results could be an interesting contribution; as written, they are not reproducible and the main claims are not supported.

major comments (5)

[§4.2 and Appendix E] The four recipe-specific metrics are described only as lists of operations (e.g., "build step dependency graph", "validate ranges per method") with no scoring formulas, thresholds, or implementation. The scores in Tables 3, 4, and 6 are therefore not reproducible, and the central claims about Phi-2 degradation and SmolLM comparability are untestable. The authors should provide the exact algorithms, release the code, and specify how each sub-score is aggregated.
[Table 3 and Tables 4/6] Recipe coherence scores fall in a narrow band of 0.02–0.12 for every model and condition, including fluent baseline outputs and degenerate fine-tuned outputs. A metric with such a compressed range cannot support claims of "marginal improvements" in coherence or of fine-tuning "degradation" in coherence. The authors should report the distribution of coherence scores and demonstrate that the metric tracks human ratings of logical flow.
[Tables 2–7] All results are point estimates over a single 500-sample evaluation, with no standard errors, confidence intervals, or significance tests. Since generation is stochastic (temperature 0.75, top-p 0.95/0.8; Appendices L and M), the observed differences—e.g., SmolLM-360M vs. 1.7B ingredient coverage 0.21 vs. 0.29 in Table 3—could be noise. The claim that the two SmolLM models are "comparable" requires repeated sampling or an appropriate statistical test.
[Appendix G, Table 10 vs. Table 3] The fine-tuned SmolLM-360M output shown in Table 10 is largely random characters, yet Table 3 reports step complexity of 0.98 and ingredient coverage of 0.16 for this model's fine-tuned version. This internal inconsistency suggests the step complexity and coverage metrics are capturing surface formatting (e.g., numbered lines, length) rather than the intended content quality. The authors must reconcile the quantitative scores with their own qualitative examples.
[§4.3 and Tables 5/7] The LLM-as-a-judge scores are presented as evidence about allergen safety and recipe quality, but no evidence is given that Qwen2.5-7B's judgments correlate with human or expert assessments. The Limitations section acknowledges stochasticity but does not address validity; a judge that is not calibrated cannot support the allergen substitution conclusions.

minor comments (5)

[Table 1] The T5-small fine-tuned BLEU-1 and BLEU-2 scores of 0.00 are suspiciously low; please verify the decoding and scoring setup for this model.
[§3.4 and Appendix K] The description of QLoRA fine-tuning is incomplete (only rank 8 is given); include quantization bit-width and other LoRA hyperparameters to improve reproducibility.
[References] Several references are missing URLs or venue details, e.g., "Microsoft Research. 2023. Phi-2" and "Qwen Team. 2024. Qwen2.5"; please complete them.
[Abstract and title] The abstract and title refer to a "Benchmark Study," but no benchmark dataset or evaluation code is publicly released; consider adding a link to the code and data.
[Appendix J] "Mixed Precision* fp16 or fp32" is ambiguous; state which precision was used for each small model.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: findings are empirical comparisons under custom metrics that are unvalidated but not definitionally identical to the conclusions.

full rationale

The paper's central results (Phi-2 ingredient-coverage drop from 0.59 to 0.30, temperature/time drop from 0.329 to 0.24, SmolLM comparability) are read directly from evaluation tables, not derived from fitted parameters or from cited prior work by the same authors. No parameter is fitted to a subset and then renamed a prediction; no uniqueness theorem or ansatz is imported from a self-citation. The custom recipe metrics in Section 4.2 and Appendix E are defined as heuristic procedures (tokenize and match ingredients, count distinct operations, build step dependency graph, validate temperature/time ranges) with no formulas or human calibration, and the Limitations section acknowledges the stochastic LLM judge and the 500-sample evaluation. This raises construct-validity and reproducibility concerns: if the metrics do not measure culinary quality, the benchmark conclusions are unsupported. But that is an empirical validity problem, not circularity, because the metric definitions do not encode the paper's conclusions by construction and the evaluations are not equivalent to the inputs. The traditional BLEU, ROUGE, and perplexity results are also reported as independent checks. Therefore no circular step can be exhibited under the required standard.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rest on two unvalidated pillars: the custom recipe-quality metrics and the LLM judge scores. The Food.com dataset and pretrained models are external inputs, but no independent evidence is provided that the scoring rules capture culinary quality. The hand-chosen hyperparameters (temperature, top-p, LoRA rank, training epochs) affect the comparative results and are not systematically optimized.

free parameters (5)

Maximum new tokens = 256
Generation hyperparameter set for all allergen-substitution systems (Appendices L, M); output length directly influences step complexity and token-based metrics.
Temperature = 0.75
Sampling temperature for all generation runs; chosen by the authors, affects diversity and coherence of generated recipes.
Top-p = 0.95 (prompt-based), 0.8 (RAG)
Nucleus sampling threshold; the paper says lowering top-p for RAG 'inadvertently resulted in lower-quality recipes' (Section 6), so this choice directly affects comparative results.
LoRA rank = 8
QLORA rank used for all large-model fine-tuning (Section 3.4); the adaptor capacity affects how much the model changes during fine-tuning.
Training epochs = 1 (large models), 1-2 (small models)
Training budget chosen due to computational limits; the authors acknowledge in Limitations that 1-2 epochs may underfit and affect fine-tuning degradation.

assumptions (5)

domain assumption The Food.com RAW_recipes dataset is a valid and representative source for training and evaluating recipe generation.
The study relies entirely on this dataset (Section 3.1) without analyzing potential biases; results may not generalize to other recipe collections.
ad hoc to paper The custom auto-evaluation metrics (ingredient coverage, step complexity, recipe coherence, temperature/time checks) measure recipe quality.
These metrics are introduced in Section 4.2 and Appendix E without validation against human judgments or established benchmarks; all central comparative claims depend on their validity.
domain assumption Qwen2.5-7B LLM-as-a-judge scores are a reliable proxy for human assessment of recipe quality and allergen safety.
The paper uses Qwen2.5-7B to score all generations (Section 4.3) and acknowledges in Section 9.2 that LLM judges are stochastic, yet treats the raw scores as meaningful comparisons.
domain assumption The pretrained base models (T5-small, GPT-2, SmolLM, Phi-2) are appropriate backbones, and their pretraining is a fixed, valid starting point.
The checkpoints are used as-is (Sections 3.3 and 3.4); no analysis of pretraining distribution or suitability for culinary text is provided.
domain assumption The allergen substitution database and retrieval rules correctly map allergens to safe, context-appropriate substitutes.
The hand-built database (Appendix F) is not clinically or nutritionally verified; substitutions could be unsafe or inappropriate in specific recipes, and the paper reports low allergen-safety judge scores.

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Cite this review

Pith. "Pith review of Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study." pith.science (2026). https://pith.science/paper/AXPVJ57A

@misc{pith2026250202028,
  author       = {Pith},
  title        = {Pith review of: Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AXPVJ57A}},
  note         = {Machine review of arXiv:2502.02028}
}

read the original abstract

This research presents an exploration and study of the recipe generation task by fine-tuning various very small language models, with a focus on developing robust evaluation metrics and comparing across different language models the open-ended task of recipe generation. This study presents extensive experiments with multiple model architectures, ranging from T5-small (Raffel et al., 2023) and SmolLM-135M(Allal et al., 2024) to Phi-2 (Research, 2023), implementing both traditional NLP metrics and custom domain-specific evaluation metrics. Our novel evaluation framework incorporates recipe-specific metrics for assessing content quality and introduces approaches to allergen substitution. The results indicate that, while larger models generally perform better on standard metrics, the relationship between model size and recipe quality is more nuanced when considering domain-specific metrics. SmolLM-360M and SmolLM-1.7B demonstrate comparable performance despite their size difference before and after fine-tuning, while fine-tuning Phi-2 shows notable limitations in recipe generation despite its larger parameter count. The comprehensive evaluation framework and allergen substitution systems provide valuable insights for future work in recipe generation and broader NLG tasks that require domain expertise and safety considerations.

Figures

Figures reproduced from arXiv: 2502.02028 by the authors.

**Figure 3.** Distribution of Tokenized Length (Ingredi [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

**Figure 1.** Occurrence rate of the 30 most Frequent In [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Experimental RAG-based Allergen Substitu [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: Comparison between Baseline and FineTuned-SmolLm360 [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]

**Figure 6.** Figure 6: Comparison between Baseline and FineTuned-SmolLm1.7B [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

**Figure 7.** Figure 7: Comparison between Baseline and FineTuned-Phi-2 I LLM-As-A-Judge Radar Charts for Rag-assisted Method [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

The Digital Sous Chef -- A Comparative Study on Fine-Tuning Language Models for Recipe Generation
cs.CL 2025-08 conditional novelty 3.0 of 10

GPT-2 large beats LSTM/RNN baselines on recipe generation metrics, but the paper's tokenization contribution lacks any ablation support.

Reference graph

Works this paper leans on

27 extracted references · 13 canonical work pages · cited by 1 Pith paper

[1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Leandro von Werra, and Thomas Wolf. 2024. Smollm - blazingly fast and remarkably powerful

work page 2024
[4]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2016. https://arxiv.org/abs/1409.0473 Neural machine translation by jointly learning to align and translate . Preprint, arXiv:1409.0473

arXiv 2016
[5]

Yu Chen, Ananya Subburathinam, Ching-Hua Chen, and Mohammed J. Zaki. 2021. https://doi.org/10.1145/3437963.3441816 Personalized food recommendation as constrained question answering over a large-scale food knowledge graph . In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, WSDM ’21. ACM

work page arXiv 2021
[6]

Prateek Chhikara, Dhiraj Chaurasia, Yifan Jiang, Omkar Masur, and Filip Ilievski. 2024. https://arxiv.org/abs/2308.14391 Fire: Food image to recipe generation . Preprint, arXiv:2308.14391

arXiv 2024
[7]

Michelle Han and Junyao Chen. 2024. https://arxiv.org/abs/2408.10532 Nutrifyai: An ai-powered system for real-time food detection, nutritional analysis, and personalized meal recommendations . Preprint, arXiv:2408.10532

work page Pith review arXiv 2024
[8]

Khang Nhut Lam, My-Khanh Thi Nguyen, Huu Trong Nguyen, Vi Trieu Huynh, Jugal Kalita, et al. 2024. Enhancing transformer-based cooking recipe generation models from text ingredients. Journal of Information & Communication Convergence Engineering, 22(4)

work page 2024

Show all 27 references

[9]

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. https://doi.org/10.18653/v1/2020.acl-main.703 BART : Denoising sequence-to-sequence pre-training for natural language generation, translatio...

2020 doi
[10]

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021. https://arxiv.org/abs/2005.11401 Retrieval-augmented generation for knowledge-int...

2021 arXiv
[11]

Peiyu Li, Xiaobao Huang, Yijun Tian, and Nitesh V Chawla. 2024. Cheffusion: Multimodal foundation model integrating recipe and food image generation. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pages 3872--3876

2024
[12]

Chin-Yew Lin. 2004. https://www.aclweb.org/anthology/W04-1013.pdf Rouge: A package for automatic evaluation of summaries . In Proceedings of the Workshop on Text Summarization Branches Out, pages 74--81. Association for Computational Linguistics

2004
[13]

Guoshan Liu, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, and Yu-Gang Jiang. 2024. Retrieval augmented recipe generation. arXiv preprint arXiv:2411.08715

2024 arXiv
[14]

Yinhong Liu, Yixuan Su, Ehsan Shareghi, and Nigel Collier. 2022 a . https://arxiv.org/abs/2212.05093 Plug-and-play recipe generation with content planning . Preprint, arXiv:2212.05093

2022 arXiv
[15]

Yixin Liu, Pengfei Liu, Dragomir Radev, and Graham Neubig. 2022 b . https://doi.org/10.18653/v1/2022.acl-long.207 BRIO : Bringing order to abstractive summarization . In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pap...

2022 doi
[16]

Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, and Julian McAuley. 2019. https://arxiv.org/abs/1909.00105 Generating personalized recipes from historical user preferences . Preprint, arXiv:1909.00105

2019 arXiv
[17]

Fnu Mohbat and Mohammed J. Zaki. 2024. https://doi.org/10.1145/3627673.3679562 Llava-chef: A multi-modal generative model for food recipes . In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM ’24, page 1711–1721. ACM

2024
[18]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. https://www.aclweb.org/anthology/P02-1040.pdf Bleu: a method for automatic evaluation of machine translation . In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 3...

2002
[19]

Matt Post. 2018. https://doi.org/10.18653/v1/W18-6319 A call for clarity in reporting BLEU scores . In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186--191, Brussels, Belgium. Association for Computational Linguistics

2018 doi
[20]

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. https://api.semanticscholar.org/CorpusID:160025533 Language models are unsupervised multitask learners

2019
[21]

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2023. https://arxiv.org/abs/1910.10683 Exploring the limits of transfer learning with a unified text-to-text transformer . Preprint, arXiv:1910.10683

2023 arXiv
[22]

Colin Raffel, Noam Shazeer, Adam Roberts, et al. 2020. http://jmlr.org/papers/v21/20-074.html Exploring the limits of transfer learning with a unified text-to-text transformer . Journal of Machine Learning Research, 21(140):1--67

2020
[23]

Microsoft Research. 2023. https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/ Phi-2: The surprising power of small language models . Accessed: 2024-12-16

2023
[24]

Amaia Salvador, Michal Drozdzal, Xavier Gir \'o -i Nieto, and Adriana Romero. 2019. Inverse cooking: Recipe generation from food images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10453--10462

2019
[25]

Qwen Team. 2024. https://qwenlm.github.io/blog/qwen2.5/ Qwen2.5: A party of foundation models

2024
[26]

A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems

2017
[27]

An Yang et al. 2024. Qwen2 technical report. arXiv preprint arXiv:2407.10671

2024 arXiv

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.

[1] [1]

online" 'onlinestring :=

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Leandro von Werra, and Thomas Wolf. 2024. Smollm - blazingly fast and remarkably powerful

work page 2024

[4] [4]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2016. https://arxiv.org/abs/1409.0473 Neural machine translation by jointly learning to align and translate . Preprint, arXiv:1409.0473

arXiv 2016

[5] [5]

Yu Chen, Ananya Subburathinam, Ching-Hua Chen, and Mohammed J. Zaki. 2021. https://doi.org/10.1145/3437963.3441816 Personalized food recommendation as constrained question answering over a large-scale food knowledge graph . In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, WSDM ’21. ACM

work page arXiv 2021

[6] [6]

Prateek Chhikara, Dhiraj Chaurasia, Yifan Jiang, Omkar Masur, and Filip Ilievski. 2024. https://arxiv.org/abs/2308.14391 Fire: Food image to recipe generation . Preprint, arXiv:2308.14391

arXiv 2024

[7] [7]

Michelle Han and Junyao Chen. 2024. https://arxiv.org/abs/2408.10532 Nutrifyai: An ai-powered system for real-time food detection, nutritional analysis, and personalized meal recommendations . Preprint, arXiv:2408.10532

work page Pith review arXiv 2024

[8] [8]

Khang Nhut Lam, My-Khanh Thi Nguyen, Huu Trong Nguyen, Vi Trieu Huynh, Jugal Kalita, et al. 2024. Enhancing transformer-based cooking recipe generation models from text ingredients. Journal of Information & Communication Convergence Engineering, 22(4)

work page 2024