分析大型语言模型中幻觉与知识冲突之间的相关性
Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models
AI总结：
通过探针技术分析LLM内部表示，发现幻觉激活模式不能完全归因于知识冲突，但探针可提升模型可解释性。
摘要：
幻觉——事实不正确或无法验证的输出——仍然是大型语言模型（LLM）最具挑战性的限制之一，尤其是在知识密集型任务中。一种提出的解释是，由固定的、过时的训练数据引起的内部知识冲突。本文研究了与知识冲突相关的内部表示是否与LLM中的幻觉行为相关。使用受两项先前工作启发的探针技术，我们分析了预定义任务中隐藏层、注意力层和MLP层的激活以及输出logits。我们在幻觉检测基准上探测了LLaMA-3-8B，并在知识冲突数据集上探测了Falcon-7B。我们的发现表明，尽管概念上相关，但幻觉激活模式不能完全简化为或由知识冲突表示解释。尽管如此，探针在多种语言和激活类型中被证明是一个稳健的工具，支持其在提高LLM可解释性方面的作用。这项工作推进了对LLM中幻觉的更广泛理解，并强调了对其内部行为进行细粒度分析的价值。
英文摘要：
Hallucinations -- factually incorrect or unverifiable outputs -- remain one of the most challenging limitations of Large Language Models (LLMs), especially in knowledge-intensive tasks. One proposed explanation is internal knowledge conflicts arising from fixed, outdated training data. This paper investigates whether internal representations linked to knowledge conflicts correlate with hallucination behaviors in LLMs. Using probing techniques inspired by two prior works, we analyzed activations from hidden, attention, and MLP layers, as well as output logits, across predefined tasks. We probed LLaMA-3-8B on hallucination detection benchmarks and Falcon-7B on a knowledge conflict dataset. Our findings show that, although conceptually related, hallucination activation patterns cannot be fully reduced to or explained by knowledge conflict representations. Nonetheless, probing proves a robust tool across multiple languages and activation types, supporting its role in improving LLM interpretability. This work advances the broader understanding of hallucinations in LLMs and underscores the value of fine-grained analysis of their internal behavior.
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。
