在长响应生成中最后 hallucinate：长文档摘要的案例研究
Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization
AI总结：
本文研究了大型语言模型在长响应生成中幻觉集中在后部的现象，并提出方法以提高长输出结尾的忠实性。
摘要：
大型语言模型（LLMs）在文本生成能力方面取得了显著进展，包括摘要等任务，通常能生成连贯且流畅的输出。然而，对源材料的忠实性仍是一个重大挑战，因为会生成幻觉。尽管广泛的研究集中在检测和减少这些不准确之处，但对生成文本中幻觉的位置分布，特别是在长输出中，却较少关注。在本工作中，我们研究了LLM在长响应生成中幻觉出现的位置，以长文档摘要作为关键案例研究。聚焦于长上下文感知的长响应生成挑战性设置，我们发现了一种一致且令人担忧的现象：幻觉倾向于在生成的长响应的后部分不成比例地集中。为了理解这种偏差，我们探索了与长序列中注意力动态和解码相关的潜在贡献因素。此外，我们还研究了减轻这种位置幻觉的方法，旨在提高长输出结尾部分的忠实性。
英文摘要：
Large Language Models (LLMs) have significantly advanced text generation capabilities, including tasks like summarization, often producing coherent and fluent outputs. However, faithfulness to source material remains a significant challenge due to the generation of hallucinations. While extensive research focuses on detecting and reducing these inaccuracies, less attention has been paid to the positional distribution of hallucination within generated text, particularly in long outputs. In this work, we investigate where hallucinations occur in LLM-based long response generation, using long document summarization as a key case study. Focusing on the challenging setting of long context-aware long response generation, we find a consistent and concerning phenomenon: hallucinations tend to concentrate disproportionately in the latter parts of the generated long response. To understand this bias, we explore potential contributing factors related to the dynamics of attention and decoding over long sequences. Furthermore, we investigate methods to mitigate this positional hallucination, aiming to improve faithfulness specifically in the concluding segments of long outputs.
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