大模型通过学习语言中的频率模式提升推理能力
LLMs are Frequency Pattern Learners in Natural Language Inference
- 发现假设中谓词出现频率高于前提，存在一致频率偏差
- 微调后模型严重依赖该偏差，在对抗样本上表现差
- 频率模式与词义蕴含相关，解释了为何能提升推理性能
尽管在自然语言推理（NLI）数据集上微调大模型可提升其推理能力，但其内在机制仍不明确。本文通过实验探究模型在微调过程中实际学习的内容。分析多个NLI数据集中前提与假设的谓词频率，发现正例中假设的谓词出现频率显著高于前提，存在稳定的频率偏差。评估标准模型与微调模型在一致偏差与对抗偏差案例上的表现，结果表明模型利用频率偏差进行推理，且在对抗样本上表现显著下降。微调后的模型对频率偏差的依赖性大幅增强，说明模型从数据中学习了这些频率模式。进一步基于WordNet计算上下位词的频率，发现频率偏差与文本蕴含存在相关性。这些发现揭示了为何学习频率模式有助于提升推理任务表现。
原文摘要 · Abstract (English)
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.
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