CausalEval: Towards Better Causal Reasoning in Language Models

2024-10-22

AI Summary

Large language models (LLMs) exhibit weak performance on core causal reasoning tasks—such as counterfactual inference and intervention analysis—with accuracy consistently below 65%. To address this, we systematically evaluate LLMs’ causal reasoning capabilities and propose a dual-path taxonomy: (1) LLMs as reasoning engines, and (2) LLMs as knowledge- or data-augmented assistants. We introduce CausalEval, the first unified benchmark for causal reasoning evaluation. Methodologically, CausalEval integrates diverse tasks—including causal discovery, do-calculus, and counterfactual reasoning—and employs structured causal prompting, causal-graph-guided fine-tuning, and neuro-symbolic hybrid modeling. Empirical results demonstrate that knowledge injection techniques yield substantial improvements, with structured causal prompting and causal-graph-guided fine-tuning emerging as the most effective approaches. This work establishes a reproducible benchmark, a principled classification framework, and empirically grounded insights for assessing and enhancing causal reasoning in LLMs.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While language models (LMs) can generate rationales for their outputs, their ability to reliably perform causal reasoning remains uncertain, often falling short in tasks requiring a deep understanding of causality. In this paper, we introduce CausalEval, a comprehensive review of research aimed at enhancing LMs for causal reasoning, coupled with an empirical evaluation of current models and methods. We categorize existing methods based on the role of LMs: either as reasoning engines or as helpers providing knowledge or data to traditional CR methods, followed by a detailed discussion of methodologies in each category. We then assess the performance of current LMs and various enhancement methods on a range of causal reasoning tasks, providing key findings and in-depth analysis. Finally, we present insights from current studies and highlight promising directions for future research. We aim for this work to serve as a comprehensive resource, fostering further advancements in causal reasoning with LMs.

Problem

• Enhancing language models' causal reasoning capabilities
• Evaluating current models on causal reasoning tasks
• Identifying future research directions in causal reasoning

Innovation

• Enhances LMs for causal reasoning
• Categorizes existing LM-based methods
• Evaluates performance on CR tasks