Title: Discovering Deep Chain-of-Thought Paths Across Broader QA: A General CoT-Decoding Framework for LLMs
Primary area: foundation or frontier models, including LLMs
Keywords: Chain-of-Thought, Decoding Strategy, Large Language Models, Two-Stage Branching

Abstract: Chain-of-Thought (CoT) prompting can enhance large language models’ (LLMs) reasoning capabilities, but it is influenced by the designer’s biases and requires designing different prompts for different tasks. Recently proposed CoT-decoding can guide the model to generate chain-of-thought-style decoding paths without prompts, but it is applicable only to questions whose answer sets or output formats are fixed. Therefore, in this paper we propose GCoT-decoding, a general decoding strategy applicable to broader QA tasks. GCoT-decoding first extends the original model output to generate a final answer—replacing the original specific answer span—and then aggregates paths of the same category based on the semantic similarity of those final answers to improve stability. We further propose an optimized two-stage branching method for generating candidate decoding paths, correcting potential error paths while ensuring result diversity. We conduct extensive experiments across diverse question‑answering tasks, obtaining competitive results that demonstrate the generality of our method. We also analyze how to choose the number of paths to balance performance and efficiency, providing effective guidance for practical applications.

TL;DR: A two-stage branching decoding strategy for Large Language Models to deliver robust performance across broader QA tasks.