小更新，大怀疑：参数高效微调是否增强幻觉检测？
Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?
AI总结：
本文研究参数高效微调对幻觉检测的影响，通过实证分析发现PEFT增强了检测能力，且主要通过重塑不确定性编码而非注入新知识。
摘要：
参数高效微调（PEFT）方法被广泛用于将大语言模型（LLMs）适应到下游任务中，并且通常被认为能提高事实正确性。然而，参数高效微调方法如何影响幻觉行为仍不够清楚，尤其是在问答数据集上。在本工作中，我们通过一项全面的实证研究，系统地探讨了PEFT对幻觉检测的影响，覆盖三个开源权重LLM基础架构和三个事实性问答基准测试。对于每个模型，我们使用七种无监督的幻觉检测方法进行评估，这些方法涵盖三种互补的途径：基于语义一致性的检测器、基于置信度的检测器以及基于熵的检测器。这种多方面的评估使我们能够表征PEFT如何在不同的检测范式中重塑不确定性。结论是，我们的实验结果表明，PEFT一致地增强了幻觉检测能力，显著提高了各种幻觉检测器的AUROC。此外，进一步使用线性探针和表示诊断分析表明，PEFT方法主要重塑了不确定性的编码和表现方式，与向模型注入新事实知识相比。
英文摘要：
Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assumed to improve factual correctness. However, how the parameter-efficient fine-tuning methods affect hallucination behavior remains insufficiently understood, especially on QA datasets. In this work, we systematically investigate the impact of PEFT on hallucination detection through a comprehensive empirical study across three open-weight LLM backbones and three fact-seeking QA benchmarks. For each model, we evaluate performance using seven unsupervised hallucination detection methods spanning three complementary approaches: semantic consistency based detectors, confidence based detectors, and entropy based detectors. This multifaceted evaluation enables us to characterize how PEFT reshapes uncertainty across different detection paradigms. In conclusion, our experimental results show that PEFT consistently strengthens hallucination detection ability, substantially improving AUROC across a wide range of hallucination detectors. Besides, further analyses using linear probes and representation diagnostics indicate that PEFT methods primarily reshapes how uncertainty is encoded and surfaced, comparing with injecting new factual knowledge into the models.
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。
