Summary of Detecting Hallucinations in Large Language Model Generation: a Token Probability Approach, by Ernesto Quevedo et al.
Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach
by Ernesto Quevedo, Jorge Yero, Rachel Koerner, Pablo Rivas, Tomas Cerny
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed supervised learning approach utilizes two simple classifiers with four numerical features derived from tokens and vocabulary probabilities obtained from other LLM evaluators. This method outperforms state-of-the-art outcomes in multiple tasks across three different benchmarks, detecting hallucinations in Large Language Models (LLMs) effectively. The paper’s significance lies in its simplicity, as it requires minimal resources and can be applied to various applications relying on LLM-generated content. By employing a straightforward approach with numerical features, the method achieves promising results, making it an attractive solution for ensuring the reliability of LLM-based systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding ways to detect when Large Language Models (LLMs) make mistakes by producing fake information. This is important because we want to be sure that LLMs are giving us accurate answers. Right now, there are some methods that can do this, but they often need a lot of computer power and use the same LLM that made the mistake in the first place. The new approach uses simple math and doesn’t require as much computer power, making it useful for many applications. |
Keywords
» Artificial intelligence » Supervised
