How well do LLMs really solve problems?
Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence
October 22, 2024
https://arxiv.org/pdf/2410.15490-
This paper introduces a new method, called Dynamic Intelligence Assessment (DIA), for evaluating the reliability and confidence of large language models (LLMs) when answering complex questions. They created a benchmark dataset with many challenging tasks to test LLMs and see how well they perform, especially when asked to solve variations of similar problems.
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Key points:
- Current ways of evaluating LLMs are too simple and don't accurately reflect how well they can solve problems consistently.
- LLMs are better at "pattern matching" than actual reasoning, often failing when a question is asked in a slightly different way.
- LLMs often struggle to assess their own limitations, attempting tasks they are not equipped to handle, especially without access to tools.
- This research highlights the importance of building LLMs that can reliably solve different types of problems and accurately know when they can't solve something, which is crucial for building trustworthy multi-agent systems.