Summary of Interrogatellm: Zero-resource Hallucination Detection in Llm-generated Answers, by Yakir Yehuda et al.
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers
by Yakir Yehuda, Itzik Malkiel, Oren Barkan, Jonathan Weill, Royi Ronen, Noam Koenigstein
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper proposes a novel method to detect hallucinations in Large Language Models (LLMs), which is crucial for their widespread adoption in various real-world scenarios. The method tackles the issue of LLMs inventing answers that sound realistic but are not factually true, and demonstrates its effectiveness through extensive evaluations across multiple datasets and LLMs, including Llama-2. Specifically, the authors study the hallucination levels of various recent LLMs and show that their method achieves a Balanced Accuracy of 81% without relying on external knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have made tremendous progress in recent years, but their impact is limited due to various reasons. One big problem is that they sometimes make up answers that sound true but aren’t actually true. This is called hallucination, and it’s a major obstacle for using LLMs in real-life situations. The researchers propose a new way to detect these hallucinations, which could help us use LLMs more effectively. |
Keywords
* Artificial intelligence * Hallucination * Llama
