Summary of Qcrd: Quality-guided Contrastive Rationale Distillation For Large Language Models, by Wei Wang et al.
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models
by Wei Wang, Zhaowei Li, Qi Xu, Yiqing Cai, Hang Song, Qi Qi, Ran Zhou, Zhida Huang, Tao Wang, Li Xiao
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel framework presented in this paper aims to enhance reasoning capabilities by distilling knowledge from large language models (LLMs) through contrastive learning. The authors propose quality-guided contrastive rationale distillation, which includes temperature sampling for positive knowledge and a self-adversarial approach for negative knowledge. The framework is designed to optimize the training process using an online-updating discriminator that assesses rationale qualities and assigns weights. Experimental results demonstrate the method’s effectiveness in yielding higher-quality rationales across multiple reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem: how to make smaller language models smarter by learning from bigger ones. The idea is to teach these small models to reason better by using both good and bad information. The researchers created a special way to do this, called quality-guided contrastive rationale distillation. They tested it on different tasks and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Distillation » Temperature
