Summary of Misr: Measuring Instrumental Self-reasoning in Frontier Models, by Kai Fronsdal and David Lindner
MISR: Measuring Instrumental Self-Reasoning in Frontier Models
by Kai Fronsdal, David Lindner
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 The proposed suite of tasks evaluates the instrumental self-reasoning ability of large language model (LLM) agents, which could improve adaptability and enable self-modification. However, this ability also poses significant risks, such as enabling deceptive alignment. The paper proposes evaluations for instrumental self-reasoning in agentic tasks across various scenarios, including self-modification, knowledge seeking, and opaque self-reasoning. State-of-the-art LLMs, including commercial and open-source systems, are used to build agents that are evaluated using the proposed tasks. Results show that instrumental self-reasoning ability emerges only in the most capable frontier models and is highly context-dependent. No model passes the most difficult evaluations, making this evaluation suitable for measuring increases in future models. The authors also open-source their evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We can learn about a new idea to test how smart language models are. These models could become better at learning and changing themselves if they’re tested in different ways. However, they might also become good at being sneaky or lying. Scientists want to know how well these models can think for themselves in real-life situations. They created some special tests to check this ability in different scenarios. The best models can only do this when given a lot of help and information. This is important because it shows us what we need to work on to make better language models. |
Keywords
» Artificial intelligence » Alignment » Large language model
