{
  "id": "2404.06349",
  "version": "v1",
  "published": "2024-04-09T14:40:08.000Z",
  "updated": "2024-04-09T14:40:08.000Z",
  "title": "CausalBench: A Comprehensive Benchmark for Causal Learning Capability of Large Language Models",
  "authors": [
    "Yu Zhou",
    "Xingyu Wu",
    "Beicheng Huang",
    "Jibin Wu",
    "Liang Feng",
    "Kay Chen Tan"
  ],
  "categories": [
    "cs.LG"
  ],
  "abstract": "Causality reveals fundamental principles behind data distributions in real-world scenarios, and the capability of large language models (LLMs) to understand causality directly impacts their efficacy across explaining outputs, adapting to new evidence, and generating counterfactuals. With the proliferation of LLMs, the evaluation of this capacity is increasingly garnering attention. However, the absence of a comprehensive benchmark has rendered existing evaluation studies being straightforward, undiversified, and homogeneous. To address these challenges, this paper proposes a comprehensive benchmark, namely CausalBench, to evaluate the causality understanding capabilities of LLMs. Originating from the causal research community, CausalBench encompasses three causal learning-related tasks, which facilitate a convenient comparison of LLMs' performance with classic causal learning algorithms. Meanwhile, causal networks of varying scales and densities are integrated in CausalBench, to explore the upper limits of LLMs' capabilities across task scenarios of varying difficulty. Notably, background knowledge and structured data are also incorporated into CausalBench to thoroughly unlock the underlying potential of LLMs for long-text comprehension and prior information utilization. Based on CausalBench, this paper evaluates nineteen leading LLMs and unveils insightful conclusions in diverse aspects. Firstly, we present the strengths and weaknesses of LLMs and quantitatively explore the upper limits of their capabilities across various scenarios. Meanwhile, we further discern the adaptability and abilities of LLMs to specific structural networks and complex chain of thought structures. Moreover, this paper quantitatively presents the differences across diverse information sources and uncovers the gap between LLMs' capabilities in causal understanding within textual contexts and numerical domains.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-04-09T14:40:08.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "large language models",
      "causal learning capability",
      "comprehensive benchmark",
      "causalbench",
      "upper limits"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}