Summary of Watch Your Steps: Observable and Modular Chains Of Thought, by Cassandra A. Cohen and William W. Cohen
Watch Your Steps: Observable and Modular Chains of Thought
by Cassandra A. Cohen, William W. Cohen
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research proposes a variant of chain of thought (CoT) prompting called Program Trace Prompting. The new approach makes explanations more observable while preserving the power and flexibility of traditional CoT prompting. In this method, few-shot CoT demonstrations are wrapped in a formal syntax based on Python, allowing for better identification of steps, input/output behavior, and replacement of explanations with chains of these formalized steps. Program Trace Prompting achieves strong results on the 23 diverse tasks in the BIG-Bench Hard benchmark and enables new types of analysis, including identifying non-local errors in CoT learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help machines learn from examples by making their explanations more understandable. The method, called Program Trace Prompting, takes existing demonstrations and turns them into a special syntax that can be analyzed. This allows researchers to identify mistakes in the learning process and improve how machines learn from examples. |
Keywords
» Artificial intelligence » Few shot » Prompting » Syntax
