Sign in Subscribe

Google Research

ReAct: Synergizing Reasoning and Acting in Language Models - Summary

The paper introduces ReAct, a novel prompt-based paradigm that synergizes reasoning and acting in language models for general task solving. ReAct generates both verbal reasoning traces and actions in an interleaved manner, allowing the model to perform dynamic reasoning to create, maintain, and adj

Self-Consistency Improves Chain of Thought Reasoning in Language Models - Summary

Self-Consistency Improves Chain of Thought Reasoning in Language Models - Summary

The paper proposes a new decoding strategy called self-consistency to improve the performance of chain-of-thought prompting in language models for complex reasoning tasks. Self-consistency first samples a diverse set of reasoning paths and then selects the most consistent answer by marginalizing ou

chain-of-thought-prompting vs standard prompting

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models - Summary

The paper explores how generating a chain of thought can improve the ability of large language models to perform complex reasoning. The authors introduce a simple method called chain-of-thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiment