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Causal Reasoning in Large Language Models: A Knowledge Graph Approach
Yejin Kim, Eojin Kang, Juae Kim, H. Howie Huang
TL;DR
The paper investigates the relative contributions of semantic retrieval and causal reasoning to LLM performance. It proposes KG-based random-walk reasoning and evaluates it on commonsense QA, finding that causal reasoning can help even with unrelated context and that combining it with relevant information yields the highest improvement.
Problem
The paper investigates the relative contributions of semantic information retrieval and causal reasoning to LLM performance, including whether relying on relevant information alone is sufficient.
Source: §1 · pp. 1–2Method
The paper proposes KG-based random-walk reasoning that follows connected nodes and edges to extract causal relationships and contextual information for commonsense QA.
Source: §1 · p. 2Results
Combining relevant information and causal reasoning yields the highest improvement; graph inference also improves performance with unrelated context, and KG random-walk outperforms embedding search in Graph Inference Only.
Source: §1 · p. 3Takeaways & Limitations
In commonsense QA, causal reasoning grounded in graph relationships can improve performance even when the provided information is unrelated to the question.
Source: §1 · p. 3The experiments are restricted to the commonsense QA task.
Source: §4 · p. 7
Abstract
from arXiv · showhide
Large language models (LLMs) typically improve performance by either retrieving semantically similar information, or enhancing reasoning abilities through structured prompts like chain-of-thought. While both strategies are considered crucial, it remains unclear which has a greater impact on model performance or whether a combination of both is necessary. This paper answers this question by proposing a knowledge graph (KG)-based random-walk reasoning approach that leverages causal relationships. We conduct experiments on the commonsense question answering task that is based on a KG. The KG inherently provides both relevant information, such as related entity keywords, and a reasoning structure through the connections between nodes. Experimental results show that the proposed KG-based random-walk reasoning method improves the reasoning ability and performance of LLMs. Interestingly, incorporating three seemingly irrelevant sentences into the query using KG-based random-walk reasoning enhances LLM performance, contrary to conventional wisdom. These findings suggest that integrating causal structures into prompts can significantly improve reasoning capabilities, providing new insights into the role of causality in optimizing LLM performance.
1 Introduction
The paper examines the relative contributions of semantic retrieval and causal reasoning to LLM performance, proposing KG-based random-walk reasoning and evaluating it on commonsense QA. Its experiments find that causal reasoning can improve performance even with unrelated information, while combining it with relevant information yields the highest improvement.
1 Introduction
- The paper asks whether semantic retrieval alone is sufficient for LLM performance, given that retrieval-augmented generation selects information by embedding similarity.It investigates the relative contributions of semantic information retrieval and causal reasoning.Source: §1 · pp. 1–2
- The proposed KG-based random-walk approach follows connected nodes and edges to extract causal relationships and contextual information for commonsense QA using ConceptNet.CommonsenseQA requires relevant information retrieval and reasoning over structured relationships.Source: §1 · p. 2
- The experiments compare relevant information alone, graph inference alone, keyword-relevant information with graph inference, and relevant information with graph inference.These settings separate the contributions of relevant information and reasoning abilities.Source: §1 · pp. 2–3
- Combining relevant information with causal reasoning yields the highest improvement; graph inference also improves performance with unrelated context, and KG random-walk outperforms embedding search in Graph Inference Only.The comparison suggests causal reasoning can contribute beyond semantically related information.Source: §1 · p. 3
2 Method
The method studies KG-based reasoning for commonsense QA without further LLM training. It formulates answer selection from a question and concept, contrasts conventional retrieval prompting with KG-based random-walk reasoning, and uses graph structure to select contextual sentences.
2 Method
- The method investigates KG-based reasoning for commonsense QA without further training on LLMs.It contrasts conventional retrieval-based prompting with the proposed KG-based reasoning approach.Source: §2 · p. 3
2.1 Problem Formulation
- Given a question and question concept, the task is to select an answer from that question’s available choices; the concept identifies a semantically similar KG node for graph exploration.The graph is heterogeneous, with entity nodes and typed edges; exploration follows edge direction.Source: §2.1 · p. 3
2.2 Retrieval-Based Prompting
- Conventional retrieval prompting ranks documents by query-document embedding similarity and conditions answer generation on retrieved documents.The query is the question and the target sequence is the answer in this commonsense QA task.Source: §2.2 · p. 4
2.3 KG-Based Random-Walk Reasoning
- KG-based reasoning finds a question-concept-matched node, verbalizes its directed connections as triples, selects the triple most similar to the question, then randomly selects the next node.The resulting sentences are combined with the question to prompt answer generation.Source: §2.3 · p. 4
3 Experiments
The experiments evaluate retrieval and KG-based random-walk reasoning on CommonsenseQA, then compare their performance and test how relevance and prompt configuration affect results.
3.1 Experimental Setup
- The zero-shot evaluation uses Llama 2-Chat without additional training or tuning, and compares retrieval and reasoning approaches using ConceptNet as their shared source.The evaluation uses 1,221 CommonsenseQA validation examples and measures accuracy.Source: §3.1 · p. 5
3.2 Results
- Combining top-1 RAG information with KG-based random-walk reasoning achieves the paper’s best performance.The comparison also reports that connected-node information is particularly effective when the number of information sentences is held constant.Source: §3.2 · p. 5, Table 1 · p. 6
- Even with a less-related anchor, random-walk reasoning improves performance for k = 2 and 3, while irrelevant information without coherent reasoning is ineffective.Table 2 defines the lower-relevance conditions by whether triples are randomly selected from the most similar node or an unrelated node.Source: §3.2 · p. 5, Table 2 · p. 6
- Performance decreases when Relevant Information or Graph prompts include more than three information sentences; prompt order and graph-edge direction also matter.Table 3 evaluates prompt configurations involving prompt order and reasoning direction.Source: §3.2 · p. 6, Table 3 · p. 6
4 Conclusion
The conclusion reports that KG-based random-walk reasoning generally outperforms relevant-information retrieval on commonsense QA, while combining both sources yields the strongest performance. The paper’s evidence is limited to the commonsense QA task.
4 Conclusion
- KG-based random-walk reasoning generally outperforms relevant-information retrieval, and combining retrieval with graph reasoning produces the greatest improvement.The experiments also report improved performance when graph reasoning provides irrelevant information.Source: §4 · p. 7
- The paper’s main limitation is that its evaluation is restricted to the commonsense QA task.The authors suggest that improving reasoning capabilities could benefit future commonsense QA performance.Source: §4 · p. 7