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English(EN) CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

新的基准测试评估大型语言模型因果发现和推理能力 · 跟踪 4 个来源

作者 PulseAugur 编辑部 · [4 个来源] ·

研究人员推出了新的基准测试，以评估大型语言模型 (LLM) 的因果发现和推理能力。CausalArena 为在各种合成和真实世界数据集上对因果发现基础模型 (CDFM) 进行基准测试提供了一个统一的框架，揭示了基于基准测试设计的显著性能变化。CausalVerify 侧重于大型语言模型执行和验证因果估计工作流的能力，发现虽然一些大型语言模型可以生成代码，但其基于执行的正确性通常较低。CausalBN-Bench 评估了大型语言模型在贝叶斯网络上的因果学习能力，表明虽然它们在简单的链状结构上表现良好，但在更大、更复杂的网络（尤其是具有对撞结构的网络）上落后于传统算法。 AI

影响 这些基准测试将推动开发具有更强大因果推理能力的大型语言模型，这对于科学发现和可靠的决策至关重要。

排序理由 多篇研究论文介绍了评估大型语言模型在因果发现和推理能力方面的新基准测试。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新的基准测试评估大型语言模型因果发现和推理能力 · 跟踪 4 个来源

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多篇研究论文介绍了评估大型语言模型在因果发现和推理能力方面的新基准测试。
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报道来源 [4]

  1. arXiv cs.LG TIER_1 English(EN) · Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye ·

    CausalArena：在基础模型时代进行因果发现的基准测试

    arXiv:2609.11897v1 Announce Type: new Abstract: Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on structural causal models (SCMs), which specify a causal…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    CausalArena：在基础模型时代进行因果发现的基准测试

    Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on structural causal models (SCMs), which specify a causal graph together with the mechanisms that generat…

  3. arXiv cs.AI TIER_1 English(EN) · Yonghong Zhang, Ricardo Correia, Isabel M. Parra, Yong Xie ·

    CausalVerify：一个基于执行的LLM因果推断工作流基准测试

    arXiv:2609.07944v1 Announce Type: new Abstract: Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recovers the target causal estimate. CausalVerify studies this verification problem for…

  4. arXiv cs.LG TIER_1 English(EN) · Yu Zhou, Xingyu Wu, Jibin Wu, Liang Feng, Kay Chen Tan ·

    CausalBN-Bench：LLM因果学习能力的综合基准测试

    arXiv:2404.06349v3 Announce Type: replace 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 lac…

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