Summary of Large Language Models Are Contrastive Reasoners, by Liang Yao
Large Language Models are Contrastive Reasoners
by Liang Yao
First submitted to arxiv on: 13 Mar 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 explores the use of contrastive prompting (CP) to improve the capabilities of pre-trained large language models (LLMs). The authors demonstrate that by adding a simple prompt, such as “Let’s give a correct and a wrong answer,” LLMs can perform complex reasoning tasks with high accuracy. Experiments show that zero-shot CP improves performance on arithmetic, commonsense, and symbolic reasoning tasks without the need for hand-crafted few-shot examples. The method surpasses state-of-the-art models in many cases and can be seamlessly integrated with existing prompting methods. This paper presents a significant improvement in LLMs’ ability to perform complex reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores ways to make large language models smarter. It shows that by adding a simple phrase, these models can understand complex ideas better. The authors tested this idea on many different types of problems and found that the results were much better than before. This new way of prompting models is easy to use and works well with other methods too. The goal is to make language models more helpful in solving real-world problems. |
Keywords
» Artificial intelligence » Few shot » Prompt » Prompting » Zero shot
