  Chain-of-thought (CoT) prompting has emerged as a powerful technique for
enhancing the reasoning capabilities of Large Language Models (LLMs) on complex
problems. Among CoT-related studies, self-consistency (Multi-path inference
with answer filtering through voting) involves generating multiple reasoning
paths using the CoT framework and then selecting the most frequently produced
outputs standing out as a concise yet competitive approach. While
self-consistency has indeed led to the improvements in LLM inference, the use
of multi-path inference also escalates deployment costs. Therefore, maintaining
the performance benefits of self-consistency inherited from multi-path
inference while reducing the inference costs holds significant value. In this
research, we conceptualize language decoding as a preference consensus game,
constructing a bi-player gaming system within each local path, and introduce
Nash Chain-of-Thought (Nash CoT). Specifically, for a given question, we
leverage LLM to autonomously select the contextually relevant template and
generate outputs guided by this template, aiming to reach Nash Equilibrium
alongside normal generation in each path. This approach allows us to achieve
comparable or improved performance compared to self-consistency while using
fewer inference paths on various inference tasks, including Arabic reasoning,
Commonsense Question answering, and Symbolic inference.
