TextGenSHAP：面向长文档文本生成的可扩展事后解释方法
TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents
AI总结：
本文提出TextGenSHAP，一种融合语言模型特定技术的高效事后解释方法，大幅加速长文档文本生成中的Shapley值计算，并展示了其在长文档问答理解和文档检索系统改进中的应用价值。
摘要：
大型语言模型（LLMs）因其日益准确的响应和连贯的推理能力，在实际应用中引起了巨大兴趣。鉴于其本质上是黑箱模型，对输入进行复杂的推理过程，对LLMs生成内容的可扩展且忠实的解释需求必然会持续增长。过去十年中，神经网络模型的可解释性取得了重大进展。其中，事后可解释性方法，尤其是Shapley值，已被证明能有效解释深度学习模型。然而，将Shapley值扩展到LLMs面临重大挑战，特别是在处理包含数千个token的长输入上下文以及自回归生成的输出序列时。此外，如何有效利用生成的解释来提升LLMs的性能，往往并不明确。在本文中，我们提出了TextGenSHAP，这是一种高效的事后解释方法，融合了针对语言模型（LM）的特定技术。我们证明，与传统的Shapley值计算相比，该方法显著提升了速度，将token级解释的处理时间从数小时缩短至数分钟，将文档级解释的处理时间缩短至仅数秒。此外，我们展示了实时Shapley值在两个重要场景中的应用：通过定位重要单词和句子，更好地理解长文档问答；以及通过提高所选段落的准确性并最终改善最终响应，来改进现有的文档检索系统。
英文摘要：
Large language models (LLMs) have attracted huge interest in practical applications given their increasingly accurate responses and coherent reasoning abilities. Given their nature as black-boxes using complex reasoning processes on their inputs, it is inevitable that the demand for scalable and faithful explanations for LLMs' generated content will continue to grow. There have been major developments in the explainability of neural network models over the past decade. Among them, post-hoc explainability methods, especially Shapley values, have proven effective for interpreting deep learning models. However, there are major challenges in scaling up Shapley values for LLMs, particularly when dealing with long input contexts containing thousands of tokens and autoregressively generated output sequences. Furthermore, it is often unclear how to effectively utilize generated explanations to improve the performance of LLMs. In this paper, we introduce TextGenSHAP, an efficient post-hoc explanation method incorporating LM-specific techniques. We demonstrate that this leads to significant increases in speed compared to conventional Shapley value computations, reducing processing times from hours to minutes for token-level explanations, and to just seconds for document-level explanations. In addition, we demonstrate how real-time Shapley values can be utilized in two important scenarios, providing better understanding of long-document question answering by localizing important words and sentences; and improving existing document retrieval systems through enhancing the accuracy of selected passages and ultimately the final responses.
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。
