Aug 25, 2024 · Chain-of-Thought (CoT) prompting enhances Large Language Models (LLMs) complex reasoning abilities by generating intermediate steps.
Aug 25, 2024 · We propose the “CoT Rerailer” to address these challenges, employing self-consistency and multi-agent debate systems to identify and rectify errors in the ...
The CoT Rerailer first selects the most logically correct Reasoning Path (RP) using consistency checks and critical evaluation by automated agents. It then ...
Sep 18, 2024 · The CoT Rerailer paper presents a promising approach for enhancing the reliability of large language models in complex reasoning tasks. The key ...
Chain-of-Thought (CoT) prompting enhances Large Language Models (LLMs) complex reasoning abilities by generating intermediate steps.
Sep 12, 2024 · Chain-of-Thought (CoT) prompting reveals that large language models are capable of performing complex reasoning via intermediate steps. CoT ...
CoT Rerailer: Enhancing the Reliability of Large Language Models in Complex Reasoning Tasks through Error Detection and Correction · Published: 17 Sept 2024, ...
We propose the CoT Rerailer to address these challenges, employing self-consistency and multi-agent debate systems to identify and rectify errors in the ...
This paper proposes and proves that LLMs also have similar self-verification abilities, and takes the conclusion obtained by CoT as one of the conditions ...
Chain of thought (CoT) is a reasoning framework that can enhance the performance of Large Language Models (LLMs) on complex inference tasks.