Summary of Enabling Weak Llms to Judge Response Reliability Via Meta Ranking, by Zijun Liu et al.
Enabling Weak LLMs to Judge Response Reliability via Meta Ranking
by Zijun Liu, Boqun Kou, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Yang Liu
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 (MR) method enables weak large language models (LLMs) to effectively assess the reliability of LLM responses, surpassing strong baselines like GPT-3.5-turbo with only five reference samples. MR pairwisely ranks target query-response pairs with multiple reference query-response pairs, unlike previous few-shot methods that rely solely on in-context learning capabilities. The results demonstrate the high potential of MR in both efficiency and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Despite large language models being strong performers across various tasks, they still have reliability issues. To help weak LLMs assess response reliability, a new method called Meta Ranking is proposed. This method compares query-response pairs with reference pairs to determine reliability. It’s shown that this approach can be effective in detecting errors, allowing weak LLMs like Phi-2 to outperform strong ones like GPT-3.5-turbo. |
Keywords
* Artificial intelligence * Few shot * Gpt
