ECCoT: A Framework for Enhancing Effective Cognition via Chain of Thought in Large Language Model

TL;DR

ECCoT is a comprehensive framework that improves the reliability and interpretability of reasoning in large language models by validating and refining their chain of thought processes using topic-aware and causal reasoning techniques.

Contribution

The paper introduces ECCoT, a novel end-to-end framework that enhances LLM reasoning by integrating topic-aware generation and causal reasoning validation methods.

Findings

ECCoT improves reasoning accuracy in LLMs.

ECCoT reduces biases and increases interpretability.

ECCoT enhances trustworthiness of AI decision-making.

Abstract

In the era of large-scale artificial intelligence, Large Language Models (LLMs) have made significant strides in natural language processing. However, they often lack transparency and generate unreliable outputs, raising concerns about their interpretability. To address this, the Chain of Thought (CoT) prompting method structures reasoning into step-by-step deductions. Yet, not all reasoning chains are valid, and errors can lead to unreliable conclusions. We propose ECCoT, an End-to-End Cognitive Chain of Thought Validation Framework, to evaluate and refine reasoning chains in LLMs. ECCoT integrates the Markov Random Field-Embedded Topic Model (MRF-ETM) for topic-aware CoT generation and Causal Sentence-BERT (CSBert) for causal reasoning alignment. By filtering ineffective chains using structured ordering statistics, ECCoT improves interpretability, reduces biases, and enhances the trustworthiness of LLM-based decision-making. Key contributions include the introduction of ECCoT, MRF-ETM for topic-driven CoT generation, and CSBert for causal reasoning enhancement. Code is released at: https://github.com/erwinmsmith/ECCoT.git.