Summary of Strong Hallucinations From Negation and How to Fix Them, by Nicholas Asher and Swarnadeep Bhar
Strong hallucinations from negation and how to fix them
by Nicholas Asher, Swarnadeep Bhar
First submitted to arxiv on: 16 Feb 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 proposed solution addresses a long-standing issue in language models (LMs), where they often generate responses that are logically incoherent. This phenomenon is referred to as “strong hallucinations.” The researchers demonstrate that these errors arise from the way LMs compute internal representations for logical operators and outputs from those representations. To mitigate this issue, the authors introduce a novel approach that treats negation as an operation over an LM’s latent representations, constraining how they evolve. This method improves model performance in cloze prompting and natural language inference tasks with negation, without requiring training on sparse negative data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models struggle to reason logically, often producing responses that are impossible because of internal inconsistencies. This problem is called “strong hallucinations.” The researchers found out why this happens: LMs compute internal representations for logical operators and outputs from those representations. They suggest a new way to handle negation in language models by treating it as an operation over their internal representations, which helps them produce more accurate responses. |
Keywords
» Artificial intelligence » Inference » Prompting
