Summary of Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models, by Yuyan Chen et al.
Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
by Yuyan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes RelD, a robust discriminator trained on the RelQA dataset, to detect hallucination in Large Language Models’ (LLMs) generated answers. RelD is capable of detecting unfaithful or inconsistent content generated by LLMs, a major drawback in question answering and dialogue systems. The proposed method is evaluated using diverse LLMs and performs well in distinguishing between in-distribution and out-of-distribution datasets. Additionally, the paper presents insights into the types of hallucinations that occur. This research contributes to reliable answer generation by LLMs and has implications for mitigating hallucination in future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can generate answers that are not true. These computer-generated answers can be very misleading. The researchers created a special tool called RelD to find when these answers are not truthful. They tested it on many different computer models and showed that it works well. This is important because we want our computers to give us good answers, not false ones. |
Keywords
» Artificial intelligence » Hallucination » Question answering
