Summary of Harnessing Large Language Models As Post-hoc Correctors, by Zhiqiang Zhong and Kuangyu Zhou and Davide Mottin
Harnessing Large Language Models as Post-hoc Correctors
by Zhiqiang Zhong, Kuangyu Zhou, Davide Mottin
First submitted to arxiv on: 20 Feb 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 This paper investigates whether Large Language Models (LLMs) can be used as post-hoc correctors to improve the performance of Machine Learning (ML) models at a minimal cost. The authors propose a training-free framework, LlmCorr, which leverages an LLM’s in-context learning capability to summarize instances where an ML model makes mistakes and suggests corrections based on correlations between primary predictions and true labels. Experimental results on text analysis and molecular predictions show that the proposed approach can improve the performance of various models by up to 39%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Can Machine Learning (ML) models get a boost in performance without requiring expensive re-training or fine-tuning? This paper looks at how Large Language Models (LLMs) can help. It shows that LLMs can be used as “correctors” to improve ML model predictions by learning from mistakes and suggesting better answers. The results are impressive, with some models improving by up to 39%. This could be a game-changer for using ML in real-world applications. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning
