Graph Elicitation for Guiding Multi-Step Reasoning in Large Language Models

TL;DR

This paper introduces GE-Reasoning, a graph-based prompting method that guides large language models to generate relevant sub-questions and answers, improving multi-step reasoning accuracy in complex question answering tasks.

Contribution

The paper proposes a novel graph elicitation approach that grounds sub-questions in knowledge triplets, reducing irrelevance and redundancy in multi-step reasoning with LLMs.

Findings

Outperforms previous CoT prompting methods on benchmark datasets

Reduces irrelevant sub-questions through knowledge grounding

Enhances multi-hop reasoning accuracy

Abstract

Chain-of-Thought (CoT) prompting along with sub-question generation and answering has enhanced multi-step reasoning capabilities of Large Language Models (LLMs). However, prompting the LLMs to directly generate sub-questions is suboptimal since they sometimes generate redundant or irrelevant questions. To deal with them, we propose a GE-Reasoning method, which directs LLMs to generate proper sub-questions and corresponding answers. Concretely, given an input question, we first prompt the LLM to generate knowledge triplets, forming a graph representation of the question. Unlike conventional knowledge triplets, our approach allows variables as head or tail entities, effectively representing a question as knowledge triplets. Second, for each triplet, the LLM generates a corresponding sub-question and answer along with using knowledge retrieval. If the prediction confidence exceeds a threshold, the sub-question and prediction are incorporated into the prompt for subsequent processing. This approach encourages that sub-questions are grounded in the extracted knowledge triplets, reducing redundancy and irrelevance. Our experiments demonstrate that our approach outperforms previous CoT prompting methods and their variants on multi-hop question answering benchmark datasets.