Summary of Output Scouting: Auditing Large Language Models For Catastrophic Responses, by Andrew Bell and Joao Fonseca
Output Scouting: Auditing Large Language Models for Catastrophic Responses
by Andrew Bell, Joao Fonseca
First submitted to arxiv on: 4 Oct 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 Recent AI safety incidents have highlighted the need for Large Language Models (LLMs) to be evaluated more thoroughly. One challenge is that LLMs often produce non-zero probability harmful outputs, making it crucial to develop strategies for identifying catastrophic responses efficiently. This paper proposes output scouting, an approach that generates semantically fluent outputs matching target probability distributions. By querying LLMs with limited attempts (e.g., 1000 times), output scouting aims to find failure responses effectively. Two LLMs were experimented upon, revealing numerous examples of catastrophic responses. The authors provide advice for practitioners implementing LLM auditing and release an open-source toolkit (https://github.com/joaopfonseca/outputscouting) that utilizes the Hugging Face transformers library. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure Large Language Models (AI systems) don’t produce harmful responses. Imagine you’re checking a computer program for mistakes, but it’s very smart and can understand language. Sometimes these programs say things that are not nice or right. This paper finds ways to help find when the program says something bad. They test two different AI programs and show how they found many examples of bad responses. The authors also give tips on how people who want to do this kind of checking can do it, and they share a special tool online that helps with this process. |
Keywords
» Artificial intelligence » Probability
