Summary of General Purpose Verification For Chain Of Thought Prompting, by Robert Vacareanu et al.
General Purpose Verification for Chain of Thought Prompting
by Robert Vacareanu, Anurag Pratik, Evangelia Spiliopoulou, Zheng Qi, Giovanni Paolini, Neha Anna John, Jie Ma, Yassine Benajiba, Miguel Ballesteros
First submitted to arxiv on: 30 Apr 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 investigates ways to enhance the reasoning abilities of Large Language Models (LLMs) by exploring different thought patterns and validating individual steps. The authors propose three principles for effective reasoning: relevance, mathematical accuracy, and logical consistency. To enforce these constraints, they introduce verifiers that ask the model to verify its own steps, as well as using perplexity as an additional verifier. The method is tested on nine datasets across four reasoning tasks, showing consistent improvements over vanilla generation and even outperforming best-of-N sampling in six cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers become better thinkers by improving how they reason. Large Language Models are very good at understanding language, but they don’t always make sense or come up with the right answers. To fix this, the authors came up with three rules: relevance (does it make sense?), math accuracy (is it correct?), and logical consistency (does it follow a logical path?). They then asked the computer to check its own steps against these rules, which helps ensure the final answer is good. The method was tested on different types of reasoning tasks and showed that it did better than just letting the computer come up with answers randomly. |
Keywords
» Artificial intelligence » Perplexity
