Summary of Glitchminer: Mining Glitch Tokens in Large Language Models Via Gradient-based Discrete Optimization, by Zihui Wu et al.
GlitchMiner: Mining Glitch Tokens in Large Language Models via Gradient-based Discrete Optimization
by Zihui Wu, Haichang Gao, Ping Wang, Shudong Zhang, Zhaoxiang Liu, Shiguo Lian
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Glitch tokens in Large Language Models (LLMs) can trigger unpredictable behaviors, threatening model reliability and safety. We propose GlitchMiner, a gradient-based discrete optimization framework that efficiently identifies glitch tokens by introducing entropy as a measure of prediction uncertainty and employing a local search strategy to explore the token space. This method outperforms existing methods in detection accuracy and adaptability, achieving over 10% average efficiency improvement. The paper contributes to the development of more robust and reliable applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GlitchMiner is a new way to find glitch tokens in Large Language Models that can cause problems. Currently, there are not many good ways to do this because different models work differently. GlitchMiner uses a special measure called entropy to figure out when something is wrong and then looks for the source of the problem. It’s much better than what we have now and will help make language models safer. |
Keywords
» Artificial intelligence » Optimization » Token
