Summary of Generation Constraint Scaling Can Mitigate Hallucination, by Georgios Kollias et al.
Generation Constraint Scaling Can Mitigate Hallucination
by Georgios Kollias, Payel Das, Subhajit Chaudhury
First submitted to arxiv on: 23 Jul 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 This paper tackles the issue of hallucinations in large language models (LLMs) by exploring explicit memory mechanisms. The authors demonstrate that scaling the readout vector in a memory-augmented LLM decoder can mitigate hallucination without requiring additional training. Their geometry-inspired method outperforms a state-of-the-art editing approach on generating Wikipedia-like biography entries, achieving better quality and runtime complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have a problem: they sometimes make up things that aren’t true! This is called hallucination. To fix this, the researchers looked at how memory works in LLMs. They found that by changing one part of the model, hallucinations can be reduced without needing to train the model again. This new method is faster and better than what others have done before. |
Keywords
» Artificial intelligence » Decoder » Hallucination
