While Large language models (LLMs) have garnered widespread applications
across various domains due to their powerful language understanding and
generation capabilities, the detection of non-factual or hallucinatory content
generated by LLMs remains scarce. Currently, one significant challenge in
hallucination detection is the laborious task of time-consuming and expensive
manual annotation of the hallucinatory generation. To address this issue, this
paper first introduces a method for automatically constructing model-specific
hallucination datasets based on existing fact-checking datasets called
AutoHall. Furthermore, we propose a zero-resource and black-box hallucination
detection method based on self-contradiction. We conduct experiments towards
prevalent open-/closed-source LLMs, achieving superior hallucination detection
performance compared to extant baselines. Moreover, our experiments reveal
variations in hallucination proportions and types among different models.

该论文提出了 AutoHall 方法，通过自相矛盾的方式自动构建模型特定的幻觉数据集，然后基于这些数据集实现了无资源和黑盒幻觉检测方法，对开源和闭源大型语言模型进行了实验证明，在幻觉检测性能上优于现有基准模型，并且发现了不同模型之间的幻觉比例和类型的差异。

AutoHall: 大型语言模型的自动幻觉数据集生成

AutoHall: Automated Hallucination Dataset Generation for Large Language  Models

Large language models (large LMs) are susceptible to producing text with
hallucinated content. Self-contradiction, where the LM generates two
contradictory sentences within the same context, is an important form of
hallucination. In this work, we present a comprehensive analysis on
self-contradiction for state-of-the-art, instruction-tuned LMs, including
evaluation, detection, and mitigation. To effectively trigger
self-contradictions, we design a framework that constrains LMs to generate
appropriate sentence pairs. Our evaluation on these sentence pairs reveals that
self-contradictions occur frequently across different LMs for both famous and
lesser-known topics. Next, we prompt the LMs to detect self-contradictions. Our
results indicate that ChatGPT and GPT-4 are able to accurately identify
self-contradictions, while Vicuna-13B struggles to do so. For example, with our
best prompting method, ChatGPT achieves 91.0% precision and 80.5% recall on the
sentence pairs generated by itself. To automatically mitigate
self-contradictions, we develop an iterative algorithm that prompts the LMs to
remove the detected self-contradictions from the generated text. Our algorithm
successfully revises the text such that self-contradictions are significantly
reduced, while maintaining its fluency and informativeness. Importantly, our
entire pipeline of triggering, detecting, and mitigating self-contradictions is
applicable to black-box LMs and does not require any external grounded
knowledge.

本文分析了自相矛盾在大型语言模型中的表现，并提出了评估，检测和缓解的方法。研究发现，自相矛盾经常发生，但我们开发的框架可以准确地检测和减少自相矛盾。