Self-reflecting Large Language Models: A Hegelian Dialectical Approach

TL;DR

This paper proposes a Hegelian dialectical framework for LLMs to enable self-reflection, combining dynamic temperature strategies and multi-agent voting to enhance scientific idea generation and reasoning capabilities.

Contribution

It introduces a novel philosophical approach for LLM self-reflection, incorporating dynamic temperature control and multi-agent voting for improved ideation and reasoning.

Findings

Enhanced scientific idea generation in LLMs.

Improved mathematical and symbolic reasoning performance.

Effective validation of ideas without domain experts.

Abstract

Investigating NLP through a philosophical lens has recently caught researchers' eyes, as it bridges computational methods with classical schools of philosophy. This paper introduces a philosophical framework inspired by the Hegelian Dialectic to enable LLMs' self-reflection, utilizing a self-dialectical approach to emulate internal critiques and synthesize new scientific ideas (spanning domains such as mathematics, physics, and more). Additionally, we explore the effect of generation temperature in LLMs by introducing a dynamic annealing approach, which encourages creativity in the early stages and gradually focuses on refinement and nuance, as well as a constant-temperature strategy. Furthermore, we implement a Multi-Agent Majority Voting (MAMV) strategy to assess the validity and novelty of the generated ideas, which proves useful in the absence of domain experts. We also evaluate the effectiveness of our method in generating novel scientific ideas and improving LLMs' reasoning capabilities. Our experiments demonstrate promising results in ideation, along with significant improvements in mathematical and symbolic reasoning.