Summary of Standards For Belief Representations in Llms, by Daniel A. Herrmann and Benjamin A. Levinstein
Standards for Belief Representations in LLMs
by Daniel A. Herrmann, Benjamin A. Levinstein
First submitted to arxiv on: 31 May 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 A unified theoretical foundation is lacking in the field of studying beliefs in large language models (LLMs), which are remarkable across domains. Researchers propose adequacy conditions for a representation in an LLM to count as belief-like, building on insights from philosophy and machine learning. The proposed criteria include accuracy, coherence, uniformity, and use, balancing theoretical considerations with practical constraints. Empirical work highlights the limitations of using individual criteria to identify belief representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are really good at doing lots of things! But scientists want to know how they think and what they believe about the world. To do this, we need a way to measure what these models believe in. Right now, we don’t have a clear plan for how to do this. This paper tries to fix that by coming up with some rules to help us understand what large language models believe. It’s like trying to figure out what someone is thinking just by looking at their words and actions. |
Keywords
» Artificial intelligence » Machine learning
