Summary of Do Llms Estimate Uncertainty Well in Instruction-following?, by Juyeon Heo et al.
Do LLMs estimate uncertainty well in instruction-following?
by Juyeon Heo, Miao Xiong, Christina Heinze-Deml, Jaya Narain
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 investigate the limitations of large language models (LLMs) in following user instructions accurately. They propose a novel evaluation framework to assess the uncertainty estimation abilities of LLMs in instruction-following tasks. The authors identify key challenges with existing benchmarks and introduce two controlled data versions for comparing uncertainty estimation methods under various conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have the potential to become personal AI assistants, but they need to be able to accurately follow user instructions. Unfortunately, recent studies show that LLMs are not reliable in this task, which is a concern for high-stakes applications. To make sure these models are trustworthy, it’s essential to estimate their uncertainty when following instructions. This paper looks at how well LLMs can do this and what methods work best. |
