Summary of Large Language Models Are Self-taught Reasoners: Enhancing Llm Applications Via Tailored Problem-solving Demonstrations, by Kai Tzu-iunn Ong et al.
Large Language Models Are Self-Taught Reasoners: Enhancing LLM Applications via Tailored Problem-Solving Demonstrations
by Kai Tzu-iunn Ong, Taeyoon Kwon, Jinyoung Yeo
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposes a novel approach to improving large language models (LLMs) by automatically generating customized demonstrations that align with target instances. The method, called SELF-TAUGHT, utilizes a problem-solving framework that produces tailored and filtered demonstrations in a zero-shot manner. This is achieved through the creation of demonstrations that are optimized for correctness, ensuring better performance than strong baselines in 15 tasks across various domains, including Alzheimer’s disease diagnosis. The paper also explores the generalizability of SELF-TAUGHT to different LLMs and prompting methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make computers smarter by teaching them new skills using human-created examples. But what if we could create these examples automatically? That’s exactly what this research does! It develops a way to generate customized examples that are specifically designed for the task at hand, ensuring they’re accurate and effective. This approach is tested on 15 different tasks, including diagnosing Alzheimer’s disease, and shows impressive results compared to existing methods. |
Keywords
» Artificial intelligence » Prompting » Zero shot
