Summary of Testing Uncertainty Of Large Language Models For Physics Knowledge and Reasoning, by Elizaveta Reganova et al.
Testing Uncertainty of Large Language Models for Physics Knowledge and Reasoning
by Elizaveta Reganova, Peter Steinbach
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 analysis assesses the performance of popular open-source Large Language Models (LLMs) and gpt-3.5 Turbo on multiple choice physics questionnaires, focusing on the relationship between answer accuracy and variability in topics related to physics. The study reveals that most models provide accurate replies when they are certain, but this is not a general behavior. The results show a broad horizontal bell-shaped distribution of accuracy and uncertainty, which intensifies as questions demand more logical reasoning from the LLM agent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can answer questions in many fields, but sometimes they make things up that aren’t true. This makes it hard to know how well they’re doing. Scientists wanted to figure out how to tell if a model is confident about its answers and how that affects how accurate those answers are. They looked at how popular open-source language models and one specific model, gpt-3.5 Turbo, did on physics questions. What they found was that when the models were sure of their answers, they were usually correct. But this wasn’t always true. The relationship between accuracy and uncertainty is important to understand because it shows us how well the models are doing. |
Keywords
» Artificial intelligence » Gpt
