CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

• URL: http://arxiv.org/abs/2404.06349v2
• Date: Fri, 27 Sep 2024 11:45:09 GMT
• Title: CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
• Authors: Yu Zhou, Xingyu Wu, Beicheng Huang, Jibin Wu, Liang Feng, Kay Chen Tan,
• Abstract summary: 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.
• Score: 27.362012903540492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals the underlying data distribution. However, the lack of a comprehensive benchmark currently limits the evaluation of LLMs' causal learning capabilities. To fill this gap, this paper develops CausalBench based on data from the causal research community, enabling comparative evaluations of LLMs against traditional causal learning algorithms. To provide a comprehensive investigation, we offer three tasks of varying difficulties, including correlation, causal skeleton, and causality identification. Evaluations of 19 leading LLMs reveal that, while closed-source LLMs show potential for simple causal relationships, they significantly lag behind traditional algorithms on larger-scale networks ($>50$ nodes). Specifically, LLMs struggle with collider structures but excel at chain structures, especially at long-chain causality analogous to Chains-of-Thought techniques. This supports the current prompt approaches while suggesting directions to enhance LLMs' causal reasoning capability. Furthermore, CausalBench incorporates background knowledge and training data into prompts to thoroughly unlock LLMs' text-comprehension ability during evaluation, whose findings indicate that, LLM understand causality through semantic associations with distinct entities, rather than directly from contextual information or numerical distributions.

Related papers

• 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)
• 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)
• How Likely Do LLMs with CoT Mimic Human Reasoning? [31.86489714330338]
  Chain-of-thought (CoT) emerges as a promising technique to elicit reasoning capabilities from Large Language Models (LLMs) In this paper, we diagnose the underlying mechanism by comparing the reasoning process of LLMs with humans. Our empirical study reveals that LLMs often deviate from a causal chain, resulting in spurious correlations and potential consistency errors.
  arXiv  Detail & Related papers  (2024-02-25T10:13:04Z)
• Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models [23.438388321411693]
  Causal graph recovery is traditionally done using statistical estimation-based methods or based on individual's knowledge about variables of interests. We propose a novel method that leverages large language models (LLMs) to deduce causal relationships in general causal graph recovery tasks.
  arXiv  Detail & Related papers  (2024-02-23T13:02:10Z)
• Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs [52.42505579545893]
  Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought explanations alongside answers. We propose a novel discriminative and generative CoT evaluation paradigm to assess LLMs' knowledge of reasoning and the accuracy of the generated CoT.
  arXiv  Detail & Related papers  (2024-02-17T05:22:56Z)
• 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)
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity [61.54815512469125]
  This survey addresses the crucial issue of factuality in Large Language Models (LLMs) As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
  arXiv  Detail & Related papers  (2023-10-11T14:18:03Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)
• From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data [19.264745484010106]
  Large Language Models (LLMs) exhibit exceptional abilities for causal analysis between concepts in numerous societally impactful domains. Recent research on LLM performance in various causal discovery and inference tasks has given rise to a new ladder in the classical three-stage framework of causality. We propose a novel framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning.
  arXiv  Detail & Related papers  (2023-06-29T12:48:00Z)
• 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)
• Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks [121.74957524305283]
  This paper proposes a novel framework named textbfSearch-in-the-Chain (SearChain) for the interaction between Information Retrieval (IR) and Large Language Model (LLM) Experiments show that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2023-04-28T10:15:25Z)

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.