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REVIEW 3 major objections 4 minor 25 references

LLMs are Frequency Pattern Learners in Natural Language Inference

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read LLMs exploit a word-frequency bias in NLI inference, and fine-tuning on NLI data amplifies that reliance.

desk verdict A real and measurable frequency bias in NLI data, but the paper's attribution of fine-tuning gains to frequency learning is not yet isolated from semantic difficulty. read the letter →

arxiv 2505.21011 v1 pith:2TRLZM4R submitted 2025-05-27 cs.CL

Liang Cheng , Zhaowei Wang , Mark Steedman This is my paper
classification cs.CL
keywords frequencybiasnaturallanguageinferenceLLMfine-tuningshortcutlearninghyponym-hypernymentailmentLevy/Holtgraphsrobustness
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

This paper claims that large language models learn a frequency pattern from natural-language-inference data rather than a general inference rule. In entailment-labeled examples, the predicate in the hypothesis tends to be more frequent in general language than the predicate in the premise; the reverse holds for non-entailment examples. The authors show that models answer correctly when a test case follows this bias and fail when it goes against it, and that fine-tuning on NLI corpora deepens the dependence. They also show that hypernyms are more frequent than their hyponyms in WordNet, so following the frequency direction often amounts to performing hyponym-to-hypernym entailment, which explains why the shortcut can help on benchmarks even though it is not robust.

What carries the argument

The central object is the frequency-bias measure $Bias(hypo, prem)=Freq(hypo)-Freq(prem)$, where $Freq$ is the average WordFreq frequency of the verbal predicates in a statement. The paper splits the Levy/Holt test set into frequency-consistent ($Levy/Holt_{\mathrm{cons}}$) and frequency-adversarial ($Levy/Holt_{\mathrm{adv}}$) subsets and compares AUC between them, before and after fine-tuning. WordNet hyponym-hypernym pairs supply the semantic anchor: because hypernyms are more frequent, the bias direction aligns with the specific-to-general entailment relation that dominates positive NLI examples.

What would settle it

A concrete check would be to train on a version of the NLI data in which premise and hypothesis predicate frequencies are balanced (by resampling or rewriting), then measure AUC on the adversarial half: if the gap between the consistent and adversarial subsets disappears while overall AUC stays high, the frequency-bias explanation is unsupported.

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Extended reading notes

Core claim

The central claim is that gains from fine-tuning LLMs on NLI corpora are largely the acquisition of a frequency heuristic. In Entail instances the hypothesis predicate is more frequent than the premise predicate, while in No-Entail instances the pattern flips. Both standard and fine-tuned LLMs make correct predictions when the test case is consistent with this bias, and their accuracy drops on frequency-adversarial cases: for example, LLaMA-3-8B fine-tuned on entailment graphs falls from 85.2 AUC on the frequency-consistent split to 62.5 on the adversarial split. Using WordNet hyponym-hypernym pairs, the paper shows hypernyms are more frequent than hyponyms, making the frequency bias a proxy for a generalization gradient that supports entailment from specific to general concepts; this is offered as the reason NLI training can improve performance while leaving models fragile when the frequency direction is reversed.

Load-bearing premise

The load-bearing premise is that splitting Levy/Holt into frequency-consistent and frequency-adversarial subsets isolates the frequency direction while holding semantic difficulty fixed; if the adversarial examples are also semantically harder, the accuracy gap could arise without the model relying on frequency.

Editorial extensions

If this is right

  • If the claim holds, NLI benchmark gains from fine-tuning are partly illusory: they reflect learning the dataset's frequency direction, not a general inference rule, so performance on reversed-frequency entailments should stay low.
  • The AUC gap between the frequency-consistent and frequency-adversarial halves becomes a diagnostic for shortcut reliance and should be reported alongside overall accuracy.
  • Fine-tuning on logically valid counterfactual entailment graphs still widens the gap, so even semantically sound training data can carry frequency regularities that models pick up.
  • Because hypernyms are more frequent than hyponyms, the frequency heuristic partially aligns with a genuine entailment relation, which explains why a shortcut can help in-distribution without being robust.

Reading between the lines

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

  • An untested extension would be a debiasing procedure that balances predicate frequencies between premises and hypotheses during training; the paper's account predicts the robustness gap on adversarial cases would close without a large loss in overall accuracy.
  • A causal intervention, such as rewriting adversarial examples to reverse the frequency direction while preserving meaning, would directly test whether the model tracks frequency rather than semantics.
  • The paper restricts its frequency measure to verbal predicates; the same mechanism may operate on noun and adjective frequencies, which could make the bias even more pervasive in sentence-level NLI.
  • The WordNet correlation suggests frequency bias is not a pure artifact but a semi-valid heuristic that becomes harmful only when the frequency gradient opposes the semantic one.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper investigates whether LLMs learn frequency-based shortcuts during NLI fine-tuning. Using WordFreq, the authors compute a bias score Bias(hypo, prem) = Freq(hypo) - Freq(prem) on verbal predicates and report that positive NLI examples tend to have higher-frequency hypotheses than premises. They evaluate base and LoRA fine-tuned LLMs on Levy/Holt, partition the test set into frequency-consistent and frequency-adversarial subsets, and report lower AUC on the adversarial subset, with larger drops after fine-tuning on Entailment Graphs, RTE, or MNLI. Finally, they show that WordNet hypernyms are more frequent than hyponyms and that hypernyms occur more often in hypotheses of entailment pairs, proposing that frequency bias tracks a generalization gradient from specific to general predicates.

Significance. If the claims hold, the paper makes a useful contribution by connecting dataset-level frequency statistics to LLM inference behavior and to the known fragility of fine-tuned models on adversarial examples. The study has concrete strengths: it evaluates several LLM families and scales, it uses an external corpus (WordFreq) rather than fitting the bias to model predictions, it includes an independent WordNet-based check of the frequency-entailment correlation, and the analysis code is released. The central limitation is that the consistent/adversarial split is not matched on independent measures of inferential difficulty, so the observed AUC gap has an alternative explanation that is not ruled out by the reported experiments.

major comments (3)
  1. [§2.2, §4.3, Table 4] The load-bearing comparison between Levy/Holt_cons and Levy/Holt_adv is confounded: the split is defined by the sign of Bias(hypo, prem) = Freq(hypo) - Freq(prem), and no evidence is provided that the two subsets are matched on semantic difficulty, naturalness, hypernym direction, or any model-independent measure of inferential complexity. As a result, the lower AUC on the adversarial subset could be produced by a model with no frequency heuristic at all if that subset is simply harder. The fine-tuning amplification claim has the same problem: the cons AUC gains of roughly 7–20 points with flat adv AUC are also what would be expected if fine-tuning improves performance on the easier, more typical half of the data. Please add matched controls (e.g., balance the subsets on human accuracy, WordNet hypernym direction, predicate frequency magnitude, sentence length, and annotator agreement, or compare against a semantic-only baseline) before attributing the gap to frequency learning.
  2. [§2.1, Table 1, Appendix C] The verbal-predicate extraction procedure is not specified, and the paper is internally inconsistent about whether sentence-level datasets are included. Table 1 reports frequency bias for RTE and MNLI, but Appendix C states that MNLI and RTE 'lack clearly defined predicates' and therefore excludes them from the hyponym/hypernym analysis. Please specify exactly how verbal predicates were identified and extracted in sentence-level premises and hypotheses (including multi-predicate sentences and negation), and either justify the inclusion of RTE/MNLI in Table 1 or move them to a clearly labeled exploratory analysis.
  3. [Abstract, §4.3, Tables 2–4] The paper uses language such as 'significantly increased reliance' and 'consistent findings' without reporting any significance tests, confidence intervals, or variability across seeds. All AUC values in Tables 4 and 7 are point estimates from single runs. Please report per-instance or per-run variability (e.g., bootstrap confidence intervals for AUC, paired tests across the consistent/adversarial subsets) so that the reader can assess whether the reported gaps are reliable.
minor comments (4)
  1. [Table 6 and §4.4] Table 6 is hard to interpret: the column header 'Hypernmys Hyponyms' is a typo, and the table does not show whether the counts refer to hypernyms in hypotheses versus hyponyms in premises, or to total counts in each class. Please make the table self-contained and align it with the text's claim about where hypernyms appear.
  2. [Throughout] The verbs 'prove' and 'demonstrate' are used too strongly for correlational behavioral evidence, e.g., in §4.3 and the conclusion. I recommend replacing them with 'provide evidence for' or 'are consistent with'.
  3. [Tables 1–3, §4.2] There are several typos and formatting inconsistencies, including 'hypotheis' and 'datsets' in Table 1, 'we reports' in §4.2, and inconsistent notation for 'Levy/Holt_cons' / 'Levy/Holt_adv'. These should be cleaned up.
  4. [§3.2, Appendix E] The fine-tuning description omits standard details such as batch size, learning-rate schedule, number of training examples, and any seed averaging; including these would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the frequency-bias split and AUC gaps are empirical measurements, not constructions from the model outputs, and the self-citations are not load-bearing.

full rationale

The paper's central derivation chain is empirical rather than definitional. The frequency bias is computed as Bias(hypo, prem) = Freq(hypo) - Freq(prem) using the external WordFreq corpus statistic, and the Levy/Holt test set is partitioned into consistent and adversarial subsets purely by the sign of that corpus-frequency difference. Model predictions are then measured on those fixed partitions, so the reported AUC gap between Levy/Holt_cons and Levy/Holt_adv is an observed behavioral difference, not an identity manufactured by the definition of the split. Similarly, the claim that fine-tuning amplifies reliance on the bias is supported by before/after comparisons of measured AUC on the same externally defined partitions, and the WordNet hyponym/hypernym frequency analysis is independent of all model predictions. The paper does cite the authors' prior work, especially Cheng et al. (2025), for the EG training data and prompt templates, but the present experiments re-evaluate the models directly rather than importing the central conclusion from that citation; the self-citations are background and not load-bearing for the frequency-reliance claim. A plausible alternative explanation that Levy/Holt_adv may also contain semantically harder or less natural examples is a validity or confound concern, not a circularity, because nothing in the construction of the partitions uses the model outputs or the target conclusion. Consequently, no step in the derivation reduces by construction to its own inputs, and the appropriate finding is no significant circularity.

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

No numeric parameter is fitted to make the central claim work; the bias is computed from the external WordFreq resource. The analysis relies on domain assumptions about frequency measurement, the validity of Levy/Holt labels, and WordNet verb pairs modeling entailment, none of which are introduced ad hoc for this paper.

assumptions (5)
  • domain assumption WordFreq corpus frequencies are a valid measure of predicate frequency in natural language and in LLM pretraining distributions.
    Invoked in Section 2.1 as the sole basis for computing predicate frequencies.
  • domain assumption The verbal predicates in premises and hypotheses can be accurately identified and compared across sentence-level datasets.
    Section 2.1 says the authors focus on verbal predicates but does not specify the extraction method.
  • domain assumption Levy/Holt labels are ground-truth entailments and the directional subset is the right evaluation set for the claim.
    Section A.2 describes Levy/Holt but does not justify why its directional subset isolates frequency learning.
  • domain assumption WordNet hyponym-hypernym pairs for verbs model a core form of textual entailment, namely generalization.
    Section 4.4 uses hypernym frequency to connect frequency bias to semantic entailment.
  • domain assumption The few-shot prompt template used at inference does not itself induce frequency-related behavior.
    Appendix B.2 describes the prompt, but no control prompt is tested.

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Cite this review

Pith. "Pith review of LLMs are Frequency Pattern Learners in Natural Language Inference." pith.science (2026). https://pith.science/paper/2TRLZM4R

@misc{pith2026250521011,
  author       = {Pith},
  title        = {Pith review of: LLMs are Frequency Pattern Learners in Natural Language Inference},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2TRLZM4R}},
  note         = {Machine review of arXiv:2505.21011}
}
read the original abstract

While fine-tuning LLMs on NLI corpora improves their inferential performance, the underlying mechanisms driving this improvement remain largely opaque. In this work, we conduct a series of experiments to investigate what LLMs actually learn during fine-tuning. We begin by analyzing predicate frequencies in premises and hypotheses across NLI datasets and identify a consistent frequency bias, where predicates in hypotheses occur more frequently than those in premises for positive instances. To assess the impact of this bias, we evaluate both standard and NLI fine-tuned LLMs on bias-consistent and bias-adversarial cases. We find that LLMs exploit frequency bias for inference and perform poorly on adversarial instances. Furthermore, fine-tuned LLMs exhibit significantly increased reliance on this bias, suggesting that they are learning these frequency patterns from datasets. Finally, we compute the frequencies of hyponyms and their corresponding hypernyms from WordNet, revealing a correlation between frequency bias and textual entailment. These findings help explain why learning frequency patterns can enhance model performance on inference tasks.

Figures

Figures reproduced from arXiv: 2505.21011 by the authors.

Figure 1
Figure 1. A sample in Levy/Holtcons and Levy/Holtadv. when the bias is reduced. The phenomenon is con￾sistently more pronounced in LLMs fine-tuned on NLI datasets compared to standard models. 4.3 Finding 3: NLI-tuned LLMs struggle with frequency-adversarial inference To further measure the impact of frequency bias, we divide the Levy/Holt dataset into two subsets: those consistent with the frequency bias (Levy/Holtcons) and t… view at source ↗

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Works this paper leans on

25 extracted references · 15 canonical work pages

  1. [1]

    URL: " 'urlintro :=

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

  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]

    Jonathan Berant, Ido Dagan, and Jacob Goldberger. 2010. Global Learning of Focused Entailment Graphs . In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1220--1229

    work page 2010

  4. [4]

    Jonathan Berant, Ido Dagan, and Jacob Goldberger. 2011. Global Learning of Typed Entailment Rules . In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 610--619

    work page 2011

  5. [5]

    Liang Cheng, Tianyi Li, Zhaowei Wang, Tianyang Liu, and Mark Steedman. 2025. Neutralizing bias in llm reasoning using entailment graphs. arXiv preprint arXiv:2503.11614

    work page Pith review arXiv 2025

  6. [6]

    Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006. The PASCAL recognising textual entailment challenge. In Machine learning challenges. evaluating predictive uncertainty, visual object classification, and recognising tectual entailment, pages 177--190. Springer

    work page 2006

  7. [7]

    DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai D...

    arXiv 2025

  8. [8]

    Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. 2023. Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence, 5(3):220--235

    2023

Show all 25 references
  1. [9]

    Xuanli He, Yuxiang Wu, Oana-Maria Camburu, Pasquale Minervini, and Pontus Stenetorp. 2024. Using natural language explanations to improve robustness of in-context learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Lo...

    2024

  2. [10]

    Xavier Holt. 2019. http://arxiv.org/abs/1907.12048 Probabilistic Models of Relational Implication . arXiv:1907.12048 [cs, stat]. ArXiv: 1907.12048

    2019 arXiv

  3. [11]

    Mohammad Javad Hosseini, Nathanael Chambers, Siva Reddy, Xavier R Holt, Shay B Cohen, Mark Johnson, and Mark Steedman. 2018. Learning Typed Entailment Graphs with Global Soft Constraints . Transactions of the Association for Computational Linguistics, 6:703--717

    2018

  4. [12]

    Mohammad Javad Hosseini, Shay B Cohen, Mark Johnson, and Mark Steedman. 2021. Open - Domain Contextual Link Prediction and its Complementarity with Entailment Graphs . In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2790--2802

    2021

  5. [13]

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. https://openreview.net/forum?id=nZeVKeeFYf9 Lo RA : Low-rank adaptation of large language models . In International Conference on Learning Representations

    2022

  6. [14]

    Omer Levy and Ido Dagan. 2016. https://doi.org/10.18653/v1/P16-2041 Annotating Relation Inference in Context via Question Answering . In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics ( Volume 2: Short Papers ) , pages 249--255, Berlin,...

    2016 doi

  7. [15]

    Dengchun Li, Naizheng Wang, Zihao Zhang, Haoyang Yin, Lei Duan, Meng Xiao, and Mingjie Tang. 2025. Dynmole: Boosting mixture of lora experts fine-tuning with a hybrid routing mechanism. arXiv preprint arXiv:2504.00661

    2025 arXiv

  8. [16]

    Tianyi Li, Mohammad Javad Hosseini, Sabine Weber, and Mark Steedman. 2022. Language models are poor learners of directional inference. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 903--921

    2022

  9. [17]

    Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, and Jianfeng Gao. 2020. Adversarial training for large neural language models. arXiv preprint arXiv:2004.08994

    2020 arXiv

  10. [18]

    Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, et al. 2024. Large language models and causal inference in collaboration: A comprehensive survey. arXiv preprint arXiv:2403.09606

    2024 arXiv

  11. [19]

    Nick Mckenna, Tianyi Li, Liang Cheng, Mohammad Hosseini, Mark Johnson, and Mark Steedman. 2023 a . Sources of hallucination by large language models on inference tasks. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2758--2774

    2023

  12. [20]

    Nick Mckenna, Tianyi Li, Mark Johnson, and Mark Steedman. 2023 b . Smoothing entailment graphs with language models. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association f...

    2023

  13. [21]

    George A. Miller. 1994. https://aclanthology.org/H94-1111/ W ord N et: A lexical database for E nglish . In H uman L anguage T echnology: Proceedings of a Workshop held at P lainsboro, N ew J ersey, M arch 8-11, 1994

    1994

  14. [22]

    Martin Schmitt and Hinrich Schütze. 2021. https://www.aclweb.org/anthology/2021.eacl-main.108 Language Models for Lexical Inference in Context . In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics : Main Volume , pages...

    2021

  15. [23]

    Robyn Speer. 2022. https://doi.org/10.5281/zenodo.7199437 rspeer/wordfreq: v3.0

    2022 doi

  16. [24]

    Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. GLUE : A multi-task benchmark and analysis platform for natural language understanding. In the Proceedings of ICLR

    2019

  17. [25]

    Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of NAACL-HLT

    2018

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