面向问答任务的指令遵循模型正确性与忠实性评估
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
AI总结：
本研究针对问答任务中传统指标无法准确评估检索增强型指令遵循模型的问题，从正确性与忠实性两个维度开展评估，提出更适配的新指标，发现该类模型正确性表现优异但易出现知识幻觉，推动相关评估更全面化。
摘要：
对于问答（QA）等信息检索任务而言，检索增强型指令遵循模型是微调方法的理想替代方案。只需将检索到的文档与指令一同前置添加到模型输入中，无需额外微调，这些模型就能适配各类信息领域和任务。尽管模型生成的回复通常自然流畅，但额外的冗余表述使得精确匹配（EM）、F1等传统QA评估指标无法可靠地准确量化模型性能。\n在本研究中，我们针对三类信息检索型QA任务，探究指令遵循模型的表现。我们结合自动评估与人工评估，从两个维度评估这些模型：1）模型满足用户信息需求的程度（正确性）；2）模型是否基于提供的知识生成回复（忠实性）。在人工评估与分析的指导下，我们指出了传统指标在正确性和忠实性评估两方面的缺陷。随后我们提出了基于词元重叠和基于模型的简单指标，能够更真实地反映这些模型的性能。我们的分析显示，指令遵循模型在正确性上具备竞争力，有时甚至优于微调模型。但这类模型难以严格遵循给定知识，回复中常出现幻觉内容。我们希望本研究能推动对QA领域指令遵循模型开展更全面的评估。相关代码和数据可在https://github.com/McGill-NLP/instruct-qa 获取。
英文摘要：
Retriever-augmented instruction-following models are attractive alternatives to fine-tuned approaches for information-seeking tasks such as question answering (QA). By simply prepending retrieved documents in its input along with an instruction, these models can be adapted to various information domains and tasks without additional fine-tuning. While the model responses tend to be natural and fluent, the additional verbosity makes traditional QA evaluation metrics such as exact match (EM) and F1 unreliable for accurately quantifying model performance. In this work, we investigate the performance of instruction-following models across three information-seeking QA tasks. We use both automatic and human evaluation to evaluate these models along two dimensions: 1) how well they satisfy the user's information need (correctness), and 2) whether they produce a response based on the provided knowledge (faithfulness). Guided by human evaluation and analysis, we highlight the shortcomings of traditional metrics for both correctness and faithfulness. We then propose simple token-overlap based and model-based metrics that reflect the true performance of these models. Our analysis reveals that instruction-following models are competitive, and sometimes even outperform fine-tuned models for correctness. However, these models struggle to stick to the provided knowledge and often hallucinate in their responses. We hope our work encourages a more holistic evaluation of instruction-following models for QA. Our code and data is available at https://github.com/McGill-NLP/instruct-qa
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
