Summary of Learning From Contrastive Prompts: Automated Optimization and Adaptation, by Mingqi Li et al.
Learning from Contrastive Prompts: Automated Optimization and Adaptation
by Mingqi Li, Karan Aggarwal, Yong Xie, Aitzaz Ahmad, Stephen Lau
First submitted to arxiv on: 23 Sep 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 proposed Learning from Contrastive Prompts (LCP) framework addresses the limitations of existing prompt optimization methods by leveraging contrastive learning to generate effective prompts for large language models (LLMs). Unlike previous approaches that rely solely on learning from incorrect samples, LCP enhances both prompt optimization and adaptation. The evaluation on the Big-Bench Hard dataset demonstrates a win rate of over 76% for LCP in prompt optimization, showcasing its adaptability across different model versions, families, and languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LCP is a new way to make large language models work better. Right now, people spend a lot of time crafting special prompts to help the models understand what to do. But this process can be slow and not very good. LCP changes that by using a special kind of learning called contrastive learning. This helps create better prompts that are more effective in getting the models to do what we want. The results show that LCP is really good at making sure the models work well, even when they’re different or in other languages. |
Keywords
» Artificial intelligence » Optimization » Prompt
