Summary of Glape: Gold Label-agnostic Prompt Evaluation and Optimization For Large Language Model, by Xuanchang Zhang et al.
GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Model
by Xuanchang Zhang, Zhuosheng Zhang, Hai Zhao
First submitted to arxiv on: 4 Feb 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 This research proposes a novel approach to evaluating prompts for large language models (LLMs) that is not reliant on manual gold labels. The authors develop a gold label-agnostic prompt evaluation (GLaPE) method, which uses self-consistency as an initial evaluation score and refines it by considering mutual consistency between prompts producing identical answers. Experimental results show that GLaPE provides reliable evaluations uniform with accuracy, even in the absence of gold labels. Additionally, the authors demonstrate the effectiveness of their approach by optimizing prompts on six popular reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how to get the best out of large language models. Currently, these models are very good at answering questions when given the right prompt. But figuring out what that right prompt is can be tricky. This study develops a new way to test prompts without needing extra information (called gold labels). The method uses something called self-consistency and mutual consistency to check if different prompts produce the same answer. It works well even without those extra labels, and it helps us create better prompts for these models. |
Keywords
* Artificial intelligence * Prompt
