Prompting or Fine-tuning? Exploring Large Language Models for Causal Graph Validation

• URL: http://arxiv.org/abs/2406.16899v2
• Date: Wed, 09 Apr 2025 04:44:48 GMT
• Title: Prompting or Fine-tuning? Exploring Large Language Models for Causal Graph Validation
• Authors: Yuni Susanti, Nina Holsmoelle,
• Abstract summary: This study explores the capability of Large Language Models to evaluate causality in causal graphs.<n>Our study compares two approaches: (1) prompting-based method for zero-shot and few-shot causal inference and, (2) fine-tuning language models for the causal relation prediction task.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study explores the capability of Large Language Models (LLMs) to evaluate causality in causal graphs generated by conventional statistical causal discovery methods-a task traditionally reliant on manual assessment by human subject matter experts. To bridge this gap in causality assessment, LLMs are employed to evaluate the causal relationships by determining whether a causal connection between variable pairs can be inferred from textual context. Our study compares two approaches: (1) prompting-based method for zero-shot and few-shot causal inference and, (2) fine-tuning language models for the causal relation prediction task. While prompt-based LLMs have demonstrated versatility across various NLP tasks, our experiments on biomedical and general-domain datasets show that fine-tuned models consistently outperform them, achieving up to a 20.5-point improvement in F1 score-even when using smaller-parameter language models. These findings provide valuable insights into the strengths and limitations of both approaches for causal graph evaluation.

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