论文标题
通过信息瓶颈解释再生
Explanation Regeneration via Information Bottleneck
论文作者
论文摘要
自然和准确地解释NLP模型的黑盒预测是自然语言产生的重要开放问题。这些自由文本的解释有望包含足够且精心挑选的证据，以形成预测的支持性论点。由于大型语言模型的生成能力卓越，因此在及时工程上构建的最新工作可以使解释生成，而无需进行特定的培训。但是，通过单通气提示产生的解释通常缺乏充分性和简洁性。为了解决这个问题，我们开发了一种信息瓶颈EIB，以产生足够和简洁的精致解释。我们的方法通过抛光验证的语言模型的单通量输出，但保留支持所解释的内容的信息来再生自由文本的解释。对两个域外任务进行的实验通过自动评估和彻底进行的人类评估来验证EIB的有效性。
Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected to contain sufficient and carefully-selected evidence to form supportive arguments for predictions. Due to the superior generative capacity of large pretrained language models, recent work built on prompt engineering enables explanation generation without specific training. However, explanation generated through single-pass prompting often lacks sufficiency and conciseness. To address this problem, we develop an information bottleneck method EIB to produce refined explanations that are sufficient and concise. Our approach regenerates the free-text explanation by polishing the single-pass output from the pretrained language model but retaining the information that supports the contents being explained. Experiments on two out-of-domain tasks verify the effectiveness of EIB through automatic evaluation and thoroughly-conducted human evaluation.
