Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

• URL: http://arxiv.org/abs/2311.11212v1
• Date: Sun, 19 Nov 2023 03:31:30 GMT
• Title: Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
• Authors: Chanhui Lee (1), Juhyeon Kim (2), Yongjun Jeong (3), Juhyun Lyu (4), Junghee Kim (4), Sangmin Lee (4), Sangjun Han (4), Hyeokjun Choe (4), Soyeon Park (4), Woohyung Lim (4), Sungbin Lim (5,6), Sanghack Lee (2,7) ((1) Department of Artificial Intelligence, Korea University, (2) Graduate School of Data Science, Seoul National University, (3) Department of Computer Science and Engineering, UNIST, (4) Data Intelligence Laboratory, LG AI Research, (5) Department of Statistics, Korea University, (6) LG AI Research, (7) SNU-LG AI Research Center)
• Abstract summary: Causal reasoning of Pre-trained Language Models (PLMs) relies solely on text-based descriptions. We propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm.
• Score: 0.2303687191203919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms.

Related papers

• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the causal reasoning abilities of large language models (LLMs) through the representative problem of inferring causal relationships from narratives. We find that even state-of-the-art language models rely on unreliable shortcuts, both in terms of the narrative presentation and their parametric knowledge.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Language Agents Meet Causality -- Bridging LLMs and Causal World Models [50.79984529172807]
  We propose a framework that integrates causal representation learning with large language models. This framework learns a causal world model, with causal variables linked to natural language expressions. We evaluate the framework on causal inference and planning tasks across temporal scales and environmental complexities.
  arXiv  Detail & Related papers  (2024-10-25T18:36:37Z)
• Counterfactual Causal Inference in Natural Language with Large Language Models [9.153187514369849]
  We propose an end-to-end causal structure discovery and causal inference method from natural language. We first use an LLM to extract the instantiated causal variables from text data and build a causal graph. We then conduct counterfactual inference on the estimated graph.
  arXiv  Detail & Related papers  (2024-10-08T21:53:07Z)
• From Pre-training Corpora to Large Language Models: What Factors Influence LLM Performance in Causal Discovery Tasks? [51.42906577386907]
  This study explores the factors influencing the performance of Large Language Models (LLMs) in causal discovery tasks. A higher frequency of causal mentions correlates with better model performance, suggesting that extensive exposure to causal information during training enhances the models' causal discovery capabilities.
  arXiv  Detail & Related papers  (2024-07-29T01:45:05Z)
• CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs [27.362012903540492]
  The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning. The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning.
  arXiv  Detail & Related papers  (2024-04-09T14:40:08Z)
• Is Knowledge All Large Language Models Needed for Causal Reasoning? [11.476877330365664]
  This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence. We propose a novel causal attribution model that utilizes do-operators" for constructing counterfactual scenarios.
  arXiv  Detail & Related papers  (2023-12-30T04:51:46Z)
• Zero-shot Causal Graph Extrapolation from Text via LLMs [50.596179963913045]
  We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. LLMs show competitive performance in a benchmark of pairwise relations without needing (explicit) training samples. We extend our approach to extrapolating causal graphs through iterated pairwise queries.
  arXiv  Detail & Related papers  (2023-12-22T13:14:38Z)
• CLadder: Assessing Causal Reasoning in Language Models [82.8719238178569]
  We investigate whether large language models (LLMs) can coherently reason about causality. We propose a new NLP task, causal inference in natural language, inspired by the "causal inference engine" postulated by Judea Pearl et al.
  arXiv  Detail & Related papers  (2023-12-07T15:12:12Z)
• Causal Inference Using LLM-Guided Discovery [34.040996887499425]
  We show that the topological order over graph variables (causal order) alone suffices for causal effect inference. We propose a robust technique of obtaining causal order from Large Language Models (LLMs) Our approach significantly improves causal ordering accuracy as compared to discovery algorithms.
  arXiv  Detail & Related papers  (2023-10-23T17:23:56Z)
• Can Large Language Models Infer Causation from Correlation? [104.96351414570239]
  We test the pure causal inference skills of large language models (LLMs) We formulate a novel task Corr2Cause, which takes a set of correlational statements and determines the causal relationship between the variables. We show that these models achieve almost close to random performance on the task.
  arXiv  Detail & Related papers  (2023-06-09T12:09:15Z)
• Causal Reasoning and Large Language Models: Opening a New Frontier for Causality [29.433401785920065]
  Large language models (LLMs) can generate causal arguments with high probability. LLMs may be used by human domain experts to save effort in setting up a causal analysis.
  arXiv  Detail & Related papers  (2023-04-28T19:00:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.