通过不确定表达下的一致性检测大模型幻觉，无需内部状态。
Black-Box Hallucination Detection via Consistency Under the Uncertain Expression
- 利用模型在不确定表达时的响应一致性判断事实性。
- 在多个数据集上优于依赖内部信息的基线方法。
- 适合无访问内部参数的黑盒场景使用。
尽管近年来语言建模取得巨大进展，大型语言模型（如GPT3）仍以生成非事实性内容著称，即所谓的“幻觉”问题。现有检测与缓解幻觉的方法通常依赖外部资源或模型内部状态（如每个词元的输出概率）。由于LLM外部API受限且外部资源有限，迫切需要建立以黑盒方法为核心的幻觉检测框架。本文通过研究模型在不确定表达下的行为，发现当模型生成事实性回答时响应具有一致性，而非事实性回答则不一致。基于此，提出一种高效的黑盒幻觉检测度量方法。实验表明，该方法在预测模型输出事实性方面优于依赖内部知识的基线方法。
原文摘要 · Abstract (English)
Despite the great advancement of Language modeling in recent days, Large Language Models (LLMs) such as GPT3 are notorious for generating non-factual responses, so-called "hallucination" problems. Existing methods for detecting and alleviating this hallucination problem require external resources or the internal state of LLMs, such as the output probability of each token. Given the LLM's restricted external API availability and the limited scope of external resources, there is an urgent demand to establish the Black-Box approach as the cornerstone for effective hallucination detection. In this work, we propose a simple black-box hallucination detection metric after the investigation of the behavior of LLMs under expression of uncertainty. Our comprehensive analysis reveals that LLMs generate consistent responses when they present factual responses while non-consistent responses vice versa. Based on the analysis, we propose an efficient black-box hallucination detection metric with the expression of uncertainty. The experiment demonstrates that our metric is more predictive of the factuality in model responses than baselines that use internal knowledge of LLMs.
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