Summary of Can Separators Improve Chain-of-thought Prompting?, by Yoonjeong Park et al.
Can Separators Improve Chain-of-Thought Prompting?
by Yoonjeong Park, Hyunjin Kim, Chanyeol Choi, Junseong Kim, Jy-yong Sohn
First submitted to arxiv on: 16 Feb 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 A novel method called COT-SEP is proposed to improve the reasoning capabilities of Large Language Models (LLMs) by strategically employing separators at the end of each exemplar in Chain-of-thought (CoT) prompting. This approach helps LLMs understand their thought processes better while reasoning, leading to significant improvements on complex tasks such as GSM8K, AQuA, and CSQA. The study also explores the effects of separator type and location on multiple LLMs, including GPT-3.5-Turbo, GPT-4, and LLaMA-2 7B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can be helped to think better by breaking down their thought processes step-by-step with separators in prompts. This makes them do better on hard tasks like understanding stories or answering tricky questions. The new method is called COT-SEP and it works well with different kinds of LLMs. |
Keywords
» Artificial intelligence » Gpt » Llama » Prompting
