New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
This paper introduces AutoRace, a fully automated method for evaluating reasoning chains generated by Large Language Models (LLMs), and LLM Reasoners, a unified formulation and library for diverse step-by-step reasoning algorithms. The authors conduct extensive analysis on the critical design elements that affect the performance of LLM reasoning.
Advancing Large Language Model Reasoning Capabilities with Preference Trees
EURUS, a suite of large language models optimized for reasoning, achieves state-of-the-art results on diverse benchmarks covering mathematics, code generation, and logical reasoning problems by leveraging ULTRAINTERACT, a newly-curated large-scale, high-quality alignment dataset designed for complex reasoning tasks.
Enhancing Large Language Model Reasoning Through Structured Prompting Techniques
Structured prompting techniques, such as Chain-of-Thought, Tree of Thoughts, and Graph of Thoughts, significantly enhance the reasoning capabilities of large language models by guiding the model's thought process through intermediate steps and structured representations.
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