Summary of Look Within, Why Llms Hallucinate: a Causal Perspective, by He Li and Haoang Chi and Mingyu Liu and Wenjing Yang
Look Within, Why LLMs Hallucinate: A Causal Perspective
by He Li, Haoang Chi, Mingyu Liu, Wenjing Yang
First submitted to arxiv on: 14 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 The abstract presents a milestone in generative AI, as large language models (LLMs) achieve success in text comprehension and generation tasks. However, they suffer from severe hallucination problems, hindering practical applications. Researchers focus on data quality, neglecting the potential link between self-attention modules and LLMs’ hallucinations. This study investigates this relationship from a causal perspective. By disabling specific self-attention layers in popular open-source LLMs, it finds that altering these layers can alleviate hallucination issues. The study paves the way for understanding and mitigating LLMs’ hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are super smart computers that can understand and create text. They’re really good at answering questions and generating new text, but they also have a problem: they often make things up that aren’t true. This is called “hallucination.” The people who made LLMs tried to fix this by looking at the data used to train them, but it didn’t work. This study looked at something different – the way these models process information. They found that if you change how they focus on certain parts of the text, it can help reduce hallucinations. This is important because it could make LLMs more reliable and useful in the future. |
Keywords
» Artificial intelligence » Hallucination » Self attention
