Summary of Benchmarking Large Language Model Uncertainty For Prompt Optimization, by Pei-fu Guo et al.
Benchmarking Large Language Model Uncertainty for Prompt Optimization
by Pei-Fu Guo, Yun-Da Tsai, Shou-De Lin
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 In this paper, researchers aim to improve uncertainty estimation in Large Language Models (LLMs) by introducing a benchmark dataset and analyzing current metrics. The study focuses on four types of uncertainty: Answer, Correctness, Aleatoric, and Epistemic. Results show that existing metrics align more with Answer Uncertainty, which measures output confidence and diversity. This highlights the need for improved metrics that consider optimization objectives to guide prompt optimization. The authors provide code and dataset availability at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can answer questions and complete tasks. But they’re not perfect – sometimes they’re really confident, but wrong! This paper helps us understand how LLMs make mistakes by creating a special test set to measure their uncertainty. The researchers looked at popular models like GPT-3.5-Turbo and Meta-Llama-3.1-8B-Instruct and found that current metrics are mostly measuring the confidence of the answer, not whether it’s actually correct or not. This is important because it means we need better ways to measure uncertainty to make LLMs more accurate. |
Keywords
» Artificial intelligence » Gpt » Llama » Optimization » Prompt
