Causality Elicitation from Large Language Models

2026-03-04

AI Summary

This work proposes the first systematic framework for treating large language models (LLMs) as implicit sources of causal knowledge, aiming to extract their latent assumptions about causal relationships among events within a given topic. The approach involves generating topic-relevant text, extracting and normalizing events, constructing binary event indicator vectors, and applying causal discovery algorithms to infer underlying causal graph structures. By representing LLMs’ internal causal beliefs in terms of testable variables and graphical models, the framework offers a novel and interpretable means of externalizing these implicit assumptions. Empirical results demonstrate that the method successfully produces a set of plausible, interpretable candidate causal graphs, thereby validating the presence of coherent causal reasoning embedded within the model’s representations.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large language models (LLMs) are trained on enormous amounts of data and encode knowledge in their parameters. We propose a pipeline to elicit causal relationships from LLMs. Specifically, (i) we sample many documents from LLMs on a given topic, (ii) we extract an event list from from each document, (iii) we group events that appear across documents into canonical events, (iv) we construct a binary indicator vector for each document over canonical events, and (v) we estimate candidate causal graphs using causal discovery methods. Our approach does not guarantee real-world causality. Rather, it provides a framework for presenting the set of causal hypotheses that LLMs can plausibly assume, as an inspectable set of variables and candidate graphs.

Problem

• causality
• large language models
• causal discovery
• causal graphs
• knowledge elicitation

Innovation

• causal discovery
• large language models
• event extraction
• causal graphs
• causality elicitation