AutoReason: Dynamic Few-Shot Reasoning with Automatic Rationale Generation for Large Language Models

#ref: arxiv: 2412.06975

Here is an in-depth explanation and analysis of the research paper titled “AutoReason: Automatic Few-Shot Reasoning Decomposition”.

Research Context and Motivation

🏗 Problem Statement: Large Language Models (LLMs) like GPT-3.5 and GPT-4 struggle with multi-step reasoning tasks requiring logical breakdown and intermediate steps. Current techniques like Chain of Thought (CoT) prompting have shown promise but are limited because:
🚀They depend on hand-crafted few-shot exemplars.
🚀They are fixed and fail to adapt to query-specific requirements.

🏗 Motivation: To enhance reasoning capabilities in LLMs without manually designed prompts and make reasoning adaptive, interpretable, and scalable to various domains.

#ref: arxiv: 2412.06975

Key Contributions

🏗 AutoReason Framework:
🚀Introduces a two-step process to automatically generate rationales (CoT reasoning traces) tailored to each query.
🚀Uses a stronger LLM (GPT-4) to generate reasoning steps (rationales) and a weaker LLM…