Summary of Self-instructed Derived Prompt Generation Meets In-context Learning: Unlocking New Potential Of Black-box Llms, by Zhuo Li et al.
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs
by Zhuo Li, Yuhao Du, Jinpeng Hu, Xiang Wan, Anningzhe Gao
First submitted to arxiv on: 3 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 This paper presents a novel approach to improving the performance of large language models (LLMs) like GPT-4. The proposed method focuses on enhancing the response quality by generating reliable derived prompts that construct informative contextual environments. This self-instructed in-context learning framework empowers LLMs to deliver more effective responses through a reinforcement learning mechanism that interacts directly with the response model during prompt generation. The approach also reduces semantic inconsistencies between refined and original prompts, ensuring better alignment with the original query. Extensive experiments demonstrate significant enhancements in response quality for both Black-Box models like GPT-4 and other LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting really good at generating responses, but they still need help to understand what people want them to say. The problem is that these models don’t always get the right prompts (the questions or topics) to work with. To fix this, researchers have come up with ways to refine those prompts, but these methods can be tricky and sometimes lead to confusion. This new approach tries to solve this issue by letting the language model teach itself how to generate better prompts that help it understand what people want. It’s like a game where the model learns from its mistakes and gets smarter over time. This makes the responses more helpful and accurate, even for complex models like GPT-4. |
Keywords
» Artificial intelligence » Alignment » Gpt » Language model » Prompt » Reinforcement learning
