Pith. sign in

REVIEW 3 major objections 7 minor 29 references

VietMed-MCQ: A Consistency-Filtered Data Synthesis Framework for Vietnamese Traditional Medicine Evaluation

T0 review · 3 major / 7 minor · reviewed 2026-08-03 · deepseek-v4-flash

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

Pith's one-line read VietMed-MCQ: a 3,190-question benchmark for Vietnamese traditional medicine, built by a teacher-student consistency filter, reports 94.2% expert approval and shows Chinese-prior LLMs beating Vietnamese-specialized models by +7.21% accuracy.

desk verdict A useful new benchmark for an underserved domain, but the headline cross-lingual claim is not yet backed because the evaluation set was filtered by the winning model family. read the letter →

arxiv 2601.03792 v2 pith:MPZJXSFR submitted 2026-01-07 cs.CL

Huynh Trung Kiet , Dao Sy Duy Minh , Nguyen Dinh Ha Duong , Le Hoang Minh Huy , Long Nguyen , Dien Dinh This is my paper · ORCID
classification cs.CL
keywords VietnameseTraditionalMedicinemultiple-choicebenchmarksyntheticdatagenerationconsistencyfilteringcross-lingualtransferretrieval-augmentedLLMevaluationlow-resourcemedicalNLP
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 VietMed-MCQ, the first comprehensive multiple-choice benchmark for Vietnamese Traditional Medicine, containing 3,190 questions generated from textbooks via retrieval-augmented generation and filtered by a teacher-student consistency check. A human expert and four students approved 94.2% of a 500-question sample, with substantial inter-rater agreement. Benchmarking seven open-source models, the authors find that general-purpose models with strong Chinese-language priors (Qwen2.5) outperform Vietnamese-specialized models by 7.21 accuracy points, suggesting that cross-lingual conceptual transfer from Traditional Chinese Medicine outweighs language-specific fine-tuning for culturally rooted medical knowledge. All models still struggle with complex diagnostic reasoning, with the best reaching only about 64% accuracy.

What carries the argument

The teacher-student consistency filter: a 70B teacher model (Llama-3.1-70B) generates a question, four options, answer, reasoning, and a quoted evidence span from a textbook context; a 32B student model (Qwen2.5-32B) answers the same question independently with the context provided. A candidate question is kept only if the student's answer matches the teacher's and if the evidence string is a literal substring of the source context. This dual-consensus gate filtered out 7.78% of candidates (269 of 3,459) without human labeling, and the remaining 3,190 questions form the benchmark.

What would settle it

Take a fresh random sample of 500 questions drawn only from the 2,690 questions that were never human-reviewed, and have the same medical expert and four students evaluate them with the paper's rubric. If the approval rate falls well below 94.2% (or inter-rater agreement drops), the consistency gate is not selecting for correctness as strongly as claimed, and the benchmark's quality and the validity of the cross-lingual accuracy gap would be called into question.

Watch

Extended reading notes

Core claim

The paper claims to establish both a usable evaluation resource and a counterintuitive finding: models trained with Chinese-language medical content transfer conceptual knowledge to Vietnamese Traditional Medicine better than models specifically tuned for Vietnamese. The evidence is the accuracy gap on VietMed-MCQ: Qwen2.5-7B-Instruct achieves 62.01% versus 55.17% for VinaLlama and 54.80% for Vistral, with the gap persisting in 3-shot settings. The authors attribute this to shared Sino-Vietnamese terminology (e.g., Am Duong for Yin-Yang) and argue that conceptual overlap between VTM and TCM enables cross-lingual transfer that dominates simple language adaptation.

Load-bearing premise

The automatic consistency gate is a valid quality filter: the paper assumes that when the student model independently gives the same answer as the teacher, the question is correct—an assumption the paper itself calls 'necessary but insufficient' because the substring evidence check cannot verify reasoning, entailment, or evidence sufficiency, and because human validation removed no questions from the final dataset.

Editorial extensions

If this is right

  • If the benchmark is sound, it provides the first standardized evaluation tool for Vietnamese Traditional Medicine, enabling future comparisons of LLMs on culturally specific medical knowledge.
  • The +7.21% advantage of Chinese-prior models implies that, for low-resource languages with shared conceptual roots, pretraining on a related high-resource language can matter more than fine-tuning on the target language.
  • The teacher-student consistency gate, despite its substring-check limitation, offers a scalable, low-cost way to filter hallucinated synthetic medical data—useful for other low-resource domains.
  • The ceiling of ~64% accuracy on VietMed-MCQ indicates that current open-source models lack the diagnostic reasoning needed for reliable VTM clinical support, setting a concrete target for improvement.
  • The paper's own observation that MCQ formats may overestimate reasoning suggests that free-response variants could show even lower model competence, reinforcing the need for harder evaluations.

Reading between the lines

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

  • The positional bias (50.3% correct answers at option B) was mitigated by shuffling during evaluation, but the dataset's internal statistics may still reward models that exploit generation artifacts; a direct test is comparing models on shuffled vs. unshuffled answer distributions.
  • The cross-lingual transfer claim might be confounded by model recency and architecture: Qwen2.5-7B is a newer model than the Llama-2-based VinaLlama, so a matched-architecture comparison would isolate the Chinese-prior effect from generational improvements.
  • Because the 500-question human validation was a post-hoc quality check that removed no questions, the quality of the 2,690 never-human-reviewed questions rests entirely on the consistency gate; a fresh validation sample from that unexamined subset would test whether the 94.2% approval rate generalizes.
  • The author's hypothesis that conceptual overlap drives transfer could be tested by evaluating the same Qwen models on a Vietnamese non-medical domain where no such overlap exists, predicting smaller or absent transfer gains.
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

3 major / 7 minor

Summary. The paper presents VietMed-MCQ, a synthetic multiple-choice question benchmark for Vietnamese Traditional Medicine. The pipeline extracts contexts from textbooks, uses Llama-3.1-70B as a teacher to generate question-answer-evidence tuples, and filters candidates by requiring Qwen2.5-32B as a student to independently produce the same answer and a substring-based evidence check. Of 3,459 candidates, 3,190 survive. A stratified human validation of 500 items reports 94.2% acceptance (Fleiss' kappa = 0.82). Benchmarking seven models shows Qwen2.5-7B at 62.01% accuracy, outperforming Vietnamese-specialized models, a result the authors attribute to cross-lingual transfer from Chinese/TCM knowledge. The paper also provides difficulty distribution, error analysis, and limitations.

Significance. If the results are robust, VietMed-MCQ would be a valuable resource for evaluating culturally specific medical NLP, and the cross-lingual transfer finding would be of broader interest. The paper's strengths include the public release of code and data, a small human validation with reported inter-annotator agreement, randomized option shuffling during evaluation, and a candid limitations section. However, the central explanatory claim—that Chinese language priors drive performance—is currently confounded by the Qwen-based filtering step and by uncontrolled model comparisons. The benchmark itself appears usable, but the stated conclusions about cross-lingual transfer need substantially stronger evidence before they can be accepted.

major comments (3)
  1. [Section 3.3 / Section 5.2] The consistency filter uses Qwen2.5-32B as the Student, and the retained dataset thus excludes the 269 candidates (7.78%) on which Qwen disagreed with the Teacher. The subsequent evaluation then reports that Qwen2.5-7B outperforms Vietnamese models by a large margin. This is not a neutral test: the benchmark has been conditioned on Qwen-family judgment. The 500-question human validation (§4.2) only assesses the correctness of retained items; it does not test whether filtering introduces a family-dependent bias. The paper itself notes in §6 that an ablation isolating the student filter would strengthen the claims, but no such experiment is reported. Please report rankings on the full candidate set, or on subsets produced by alternative student models (e.g., a non-Qwen student), and show that the Qwen advantage persists. Without this, the cross-lingual transfer conclusion is confounded.
  2. [Section 5.2 / Table 3 / Abstract] The reported '+7.21% accuracy' over the best Vietnamese model is arithmetically inconsistent. Table 3 gives Qwen2.5-7B 62.01%, VinaLlama 55.17%, and Vistral 54.80%. The gap over the best Vietnamese model (VinaLlama) is 6.84 percentage points; 7.21 is the gap over Vistral only. The abstract repeats the 7.21 figure without specifying the baseline. Moreover, the cross-lingual transfer interpretation is not supported by a controlled comparison: VinaLlama and Vistral differ from Qwen in base architecture, pretraining corpus, and instruction-tuning recipe, and Llama-3-8B (which lacks Chinese priors) also outperforms both Vietnamese models. Please correct the number and test matched models (e.g., the same base with and without Vietnamese specialization) or explicitly discuss the confounds.
  3. [Sections 3.3, 4.2, and 6] The quality claim for the dataset rests on a weak automatic gate. Eq. (3) only checks substring containment; as the authors state in §6, it 'cannot verify semantic entailment... cannot assess reasoning correctness... cannot check evidence sufficiency.' Only 500 of 3,190 questions (15.7%) received human review, and this review was post-hoc—no items were removed (see the conflicting statements in §4.2). Consequently, 2,690 questions are supported only by dual-model agreement, which is not a gold standard. The abstract and conclusion describe the dataset as 'high-quality' and 'clinically verified' without qualification. Please either expand human validation to a larger sample (including rejected candidates) or qualify the quality claims in the abstract and conclusion to reflect the actual evidence level.
minor comments (7)
  1. [Section 4.2] Fleiss' kappa = 0.82 is labeled 'substantial'; under Landis and Koch, 0.81–1.00 is 'almost perfect.' Correct the terminology.
  2. [Section 4.2] The text says 'Questions receiving at least three Accept votes... were retained' but immediately adds 'no questions were removed based on validation results.' Clarify whether the validation was used to filter or was purely post-hoc.
  3. [Abstract / Section 1] 'Clinically verified' and 'expert approval' overstate the role of the four student evaluators; suggest 'human-validated' with evaluator composition specified.
  4. [Section 4.3, footnote 2] Difficulty thresholds are 'not reproducible' because they were adjusted to target proportions. Provide the final thresholds or an automated classifier to make the difficulty labels reproducible.
  5. [Section 6] The sentence 'The proportion of Hard questions (26.3' is truncated; complete the sentence.
  6. [Section 5.4] The qualitative error analysis lacks methodological detail (how many examples, selection criteria). Add a brief protocol.
  7. [References] AfriMedQA appears in the text but AfriMed-QA in the reference list; align the spelling.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the dataset is independently human-validated and the Qwen-filter/evaluation overlap is a confound, not a by-construction reduction.

full rationale

The paper's derivation chain is not circular. The retained dataset is defined by the Student agreement rule (Eq. 2) and the substring evidence check (Eq. 3), but these equations only select candidate items; they do not assign the accuracy values in Table 3. The 94.2% expert approval comes from a separate human validation of 500 stratified samples, and the paper explicitly labels the evidence gate 'a necessary but insufficient quality gate' (Section 6), so the quality claim is not derived from the filter alone. The Qwen-family overlap between the Student filter (Qwen2.5-32B) and the top-scoring evaluated models (Qwen2.5-7B/32B) is a legitimate validity threat to the cross-lingual-transfer interpretation, and the paper's own Future Work calls for 'Ablation studies isolating the student filter’s impact on item quality.' However, this is selection/confounding bias rather than equivalence-by-construction: Qwen2.5-7B's 62.01% accuracy is not an algebraic consequence of Qwen2.5-32B's agreement rule, and the comparison could in principle have gone the other way. No load-bearing self-citations, no imported uniqueness theorems, no ansatz smuggled via citation, and no fitted parameter renamed as a prediction appear in the manuscript.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central quality claim rests on unproven premises: student-teacher agreement implies correctness, substring containment implies grounding, and a 500-question post-hoc sample with no removals estimates full-dataset quality. The difficulty distribution is a tuned artifact. No invented entities: the framework and dataset are pipeline artifacts, not postulated objects requiring independent falsifiable handles.

free parameters (3)
  • Difficulty classification thresholds (Easy/Medium/Hard) = unreported; rebalanced to 22.0/51.8/26.3%
    Footnote 2 (Sec 4.3): thresholds were 'adjusted to achieve target proportions' and are explicitly 'not reproducible.' These labels drive per-difficulty evaluation claims.
  • Chunk size and chunk overlap = 2000 chars / 200 chars
    Hand-chosen in Appendix D.1; affects question granularity and evidence span, but is conventional and not tuned to the headline result.
  • Teacher/Student sampling temperatures = teacher 0.6 (top-p 0.9); student 0.2 (top-p 0.85)
    Hand-chosen (Appendices D.2/D.3) to balance 'factual accuracy' vs 'stricter validation'; standard hyperparameter choices.
assumptions (5)
  • domain assumption Student-Teacher answer agreement (a_hat = a) implies the answer key is correct.
    Section 3.3 retains samples solely on this consensus; no independent ground truth for the 2,690 un-reviewed questions. The paper's Limitations (Sec 6) concede reasoning correctness is not validated.
  • domain assumption Literal substring containment Valid(e)=I(e subset of c_i) is sufficient evidence grounding.
    Equation (3) is the entire fact-check; Section 6 calls it 'a necessary but insufficient quality gate' and lists entailment/sufficiency failures.
  • domain assumption The 500-question stratified human sample (94.2% accept) estimates the quality of all 3,190 questions.
    Section 4.2 states validation is a 'post-hoc quality indicator,' not a filter; no questions were removed, so the estimate must be extrapolated to un-reviewed items.
  • domain assumption Zero-shot MCQ accuracy after option shuffling isolates domain knowledge from position and format biases.
    Section 5.1; shuffling handles the 50.3% Option-B bias, but Mistral's 9.5% validity shows format compliance is a separate confound, and MCQ surface patterns remain (Chen et al. 2024 caveat cited).
  • domain assumption 'Authoritative VTM textbooks and clinical guidelines' (unnamed) are a representative and correct source of VTM knowledge.
    Section 3.1 gives no titles, editions, or provenance, so corpus representativeness and licensing cannot be checked.

how reviews work

0 comments
Cite this review

Pith. "Pith review of VietMed-MCQ: A Consistency-Filtered Data Synthesis Framework for Vietnamese Traditional Medicine Evaluation." pith.science (2026). https://pith.science/paper/MPZJXSFR

@misc{pith2026260103792,
  author       = {Pith},
  title        = {Pith review of: VietMed-MCQ: A Consistency-Filtered Data Synthesis Framework for Vietnamese Traditional Medicine Evaluation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MPZJXSFR}},
  note         = {Machine review of arXiv:2601.03792}
}
read the original abstract

Large Language Models (LLMs) have demonstrated remarkable proficiency in general medical domains. However, their performance significantly degrades in specialized, culturally specific domains such as Vietnamese Traditional Medicine (VTM), primarily due to the scarcity of high-quality, structured benchmarks. In this paper, we introduce VietMed-MCQ, a novel multiple-choice question dataset generated via a Retrieval-Augmented Generation (RAG) pipeline with an automated consistency check mechanism. Unlike previous synthetic datasets, our framework incorporates a dual-model validation approach to ensure reasoning consistency through independent answer verification, though the substring-based evidence checking has known limitations. The complete dataset of 3,190 questions spans three difficulty levels and underwent validation by one medical expert and four students, achieving 94.2 percent approval with substantial inter-rater agreement (Fleiss' kappa = 0.82). We benchmark seven open-source models on VietMed-MCQ. Results reveal that general-purpose models with strong Chinese priors outperform Vietnamese-centric models, highlighting cross-lingual conceptual transfer, while all models still struggle with complex diagnostic reasoning. Our code and dataset are publicly available to foster research in low-resource medical domains.

Figures

Figures reproduced from arXiv: 2601.03792 by the authors.

Figure 1
Figure 1. The VietMed-MCQ Data Synthesis Framework. We employ a Teacher model (MT ) to generate candidate questions from medical contexts, followed by a Student model (MS) that validates the answers blindly. Only samples achieving teacher-student consensus are retained. 1. Answer Consistency (Reasoning Check). We feed the generated question q, options O, and the original context ci to the Student model, masking the Teacher’s … view at source ↗
Figure 2
Figure 2. Distribution of issues identified in human validation. Among the 29 flagged questions (5.8% of the 500 validation sample), the most common issues were ambiguous distractor phrasing (38%), outdated medical terminology (29%), and overly technical lan￾guage requiring clarification (21%). Remaining 12% included factual errors or multiple correct answers. 4.3 Quality Metrics Reasoning Depth. A distinguishing feature of o… view at source ↗
Figure 3
Figure 3. Distribution of answer keys. The prevalence [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparative performance of evaluated models [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

29 extracted references · 1 canonical work pages

  1. [1]

    AI@Meta. 2024. https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md L lama 3 model card . Meta AI Research

    2024

  2. [2]

    Bowman, Gabor Angeli, Christopher Potts, and Christopher D

    Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. https://doi.org/10.18653/v1/D15-1075 A large annotated corpus for learning natural language inference . In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 632--642, Lisbon, Portugal. Association for Computational Linguistics

    doi:10.18653/v1/d15-1075 2015

  3. [3]

    Harrison Chase. 2022. L ang C hain: Building applications with LLM s through composability. https://github.com/langchain-ai/langchain. GitHub Repository

    2022

  4. [4]

    Yifan Chen, Ananya Pal, Hamid Palangi, and 1 others. 2024. https://arxiv.org/abs/2405.09384 F ree M ed QA : Benchmarking applied medical knowledge with free-response evaluations . arXiv preprint arXiv:2405.09384

    arXiv 2024

  5. [5]

    Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzm \'a n, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. https://doi.org/10.18653/v1/2020.acl-main.747 Unsupervised cross-lingual representation learning at scale . In Proceedings of the 58th Annual Meeting of the Association for Comp...

    doi:10.18653/v1/2020.acl-main.747 2020

  6. [6]

    Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. QL o RA : Efficient finetuning of quantized LLM s. In Advances in Neural Information Processing Systems, volume 36, pages 10088--10115

    2023

  7. [7]

    Yanqiao Guo, Xiaoqing Chen, and 1 others. 2023. https://arxiv.org/abs/2310.15864 B ian C ang: A traditional chinese medicine large language model . arXiv preprint arXiv:2310.15864

    arXiv 2023

  8. [8]

    Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. https://arxiv.org/abs/1503.02531 Distilling the knowledge in a neural network . arXiv preprint arXiv:1503.02531

    arXiv 2015

Show all 29 references
  1. [9]

    Junjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig, Orhan Firat, and Melvin Johnson. 2020. XTREME : A massively multilingual multi-task benchmark for evaluating cross-lingual generalization. In Proceedings of the 37th International Conference on Machine Learning, pages...

    2020

  2. [10]

    Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1--38

    2023

  3. [11]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, and 1 others. 2023. https://arxiv.org/abs/2310.06825 M istral 7 B . arXiv preprint arXiv:2310.06825

    2023 arXiv

  4. [12]

    Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hsuan Weng, Hanyi Fang, and Peter Szolovits. 2021. https://doi.org/10.18653/v1/2021.acl-long.263 What disease does this patient have? a large-scale dataset with symptom-to-diagnosis and treatment rationales . In Proceedings of the 59th...

    2021 doi

  5. [13]

    Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019. https://doi.org/10.18653/v1/D19-1259 P ub M ed QA : A dataset for biomedical research question answering . In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing an...

    2019 doi

  6. [14]

    Pollard, and Roger G

    Alistair Johnson, Tom J. Pollard, and Roger G. Mark. 2023. Biomedical text mining and natural language processing in low-resource languages. Journal of Biomedical Informatics, 138:104280

    2023

  7. [15]

    Richard Landis and Gary G

    J. Richard Landis and Gary G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics, 33(1):159--174

    1977

  8. [16]

    u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K \"u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \"a schel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-augmented generation for knowledge-intensive NLP tasks. In Proc...

    2020

  9. [17]

    Ziyang Li, Mengzhou Wang, and 1 others. 2024. https://arxiv.org/abs/2402.08159 TCM - L adder: A benchmark for evaluating large language models on traditional chinese medicine . arXiv preprint arXiv:2402.08159

    2024 arXiv

  10. [18]

    Chien Van Nguyen and Liem Tan Nguyen. 2024. V istral: V ietnamese M istral 7 B . https://huggingface.co/Viet-Mistral/Vistral-7B-Chat. Hugging Face Model Hub

    2024

  11. [19]

    Van Thanh Nguyen and Minh Hoang Le. 2018. Traditional medicine knowledge representation and reasoning: A survey. In Proceedings of the 10th Asian Conference on Intelligent Information and Database Systems, pages 365--375. Springer

    2018

  12. [20]

    Quoc Long Pham and Thi Hong Nguyen. 2020. Vietnamese traditional medicine: Historical development and contemporary practice. Journal of Ethnopharmacology, 252:112589

    2020

  13. [21]

    Qwen Team . 2024. https://arxiv.org/abs/2409.12191 Q wen2.5 technical report . arXiv preprint arXiv:2409.12191

    2024 arXiv

  14. [22]

    Atnafu Lambebo Tonja, Vijeta Mullachery, and 1 others. 2024. A fri M ed- QA : A pan-african multi-specialty medical question answering benchmark. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics

    2024

  15. [23]

    Quan Tran, Long Phan, Tuan Vo, and Hieu Nguyen. 2023. https://arxiv.org/abs/2312.11011 V ina LL a MA : LL a MA -based V ietnamese large language models . arXiv preprint arXiv:2312.11011

    2023 arXiv

  16. [24]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824--24837

    2022

  17. [25]

    Qian Zhang, Junnan Chen, and 1 others. 2024 a . https://arxiv.org/abs/2402.09856 MM ed B ench: A multilingual medical benchmark for large language models . arXiv preprint arXiv:2402.09856

    2024 arXiv

  18. [26]

    Yucheng Zhang, Zhe Liu, and 1 others. 2024 b . https://arxiv.org/abs/2403.12807 M ed RGAG : Retrieval-augmented generation with adaptive selection for medical question answering . arXiv preprint arXiv:2403.12807

    2024 arXiv

  19. [27]

    Gonzalez, and Ion Stoica

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging LLM -as-a-judge with MT - B ench and C hatbot A rena. In Advances in Neural Informatio...

    2023

  20. [28]

    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...

  21. [29]

    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 gl...

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.