大型語言模型能否捕捉人類註釋者的分歧？

摘要

在自然语言处理（NLP）领域，人工标注的变异性（即标注分歧）是普遍存在的，这往往反映了任务的主观性和样本的模糊性等重要信息。尽管大型语言模型（LLMs）越来越多地用于自动标注以减少人力投入，但其评估通常集中于预测多数投票的“真实”标签。然而，这些模型是否也能捕捉到具有信息价值的人工标注变异性，目前尚不明确。本研究通过广泛评估LLMs在无法获取重复人工标注的情况下预测标注分歧的能力，填补了这一空白。我们的结果表明，LLMs在建模分歧方面存在困难，这一点可能被基于多数标签的评估所忽视。值得注意的是，虽然RLVR（基于可验证奖励的强化学习）风格的推理通常能提升LLM的性能，但在分歧预测方面却降低了表现。我们的发现强调了评估和改进LLM标注者在分歧建模方面能力的迫切需求。代码与数据详见https://github.com/EdisonNi-hku/Disagreement_Prediction。

English

Human annotation variation (i.e., annotation disagreements) is common in NLP and often reflects important information such as task subjectivity and sample ambiguity. While Large Language Models (LLMs) are increasingly used for automatic annotation to reduce human effort, their evaluation often focuses on predicting the majority-voted "ground truth" labels. It is still unclear, however, whether these models also capture informative human annotation variation. Our work addresses this gap by extensively evaluating LLMs' ability to predict annotation disagreements without access to repeated human labels. Our results show that LLMs struggle with modeling disagreements, which can be overlooked by majority label-based evaluations. Notably, while RLVR-style (Reinforcement learning with verifiable rewards) reasoning generally boosts LLM performance, it degrades performance in disagreement prediction. Our findings highlight the critical need for evaluating and improving LLM annotators in disagreement modeling. Code and data at https://github.com/EdisonNi-hku/Disagreement_Prediction.

大型語言模型能否捕捉人類註釋者的分歧？

Can Large Language Models Capture Human Annotator Disagreements?

摘要

Support