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        <title>Can Large Language Models Infer Causal Relationships from Real-World Text?</title>
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    <abstract>Understanding and inferring causal relationships from texts is a core aspect of human cognition and is essential for advancing large language models (LLMs) towards artificial general intelligence. Existing work evaluating LLM causal reasoning primarily relies on synthetic or simplified texts with explicitly stated causal relationships. These texts typically feature short passages and few causal relations, failing to reflect the complexities of real-world reasoning. In this paper, we investigate whether LLMs are capable of inferring causal relationships from real-world texts. We develop a benchmark drawn from real-world academic literature, which includes diverse texts with respect to length, complexity (different levels of explicitness, number of causal events and relationships), and domain. To the best of our knowledge, our benchmark is the first-ever real-world dataset for this task. Our experiments on this dataset show that LLMs face significant challenges in inferring causal relationships from real-world text, with the best-performing model achieving an average F₁ score of only 0.535. Through systematic analysis across aspects of real-world text (explicitness, number of causal events and relationships, length of text, domain), our benchmark offers targeted insights for further research into advancing LLM causal reasoning. Our code and dataset can be found at https://github.com/Ryan-Saklad/ReCITE.</abstract>
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%0 Conference Proceedings
%T Can Large Language Models Infer Causal Relationships from Real-World Text?
%A Saklad, Ryan
%A Chadha, Aman
%A Pavlov, Oleg V.
%A Moraffah, Raha
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F saklad-etal-2026-large
%X Understanding and inferring causal relationships from texts is a core aspect of human cognition and is essential for advancing large language models (LLMs) towards artificial general intelligence. Existing work evaluating LLM causal reasoning primarily relies on synthetic or simplified texts with explicitly stated causal relationships. These texts typically feature short passages and few causal relations, failing to reflect the complexities of real-world reasoning. In this paper, we investigate whether LLMs are capable of inferring causal relationships from real-world texts. We develop a benchmark drawn from real-world academic literature, which includes diverse texts with respect to length, complexity (different levels of explicitness, number of causal events and relationships), and domain. To the best of our knowledge, our benchmark is the first-ever real-world dataset for this task. Our experiments on this dataset show that LLMs face significant challenges in inferring causal relationships from real-world text, with the best-performing model achieving an average F₁ score of only 0.535. Through systematic analysis across aspects of real-world text (explicitness, number of causal events and relationships, length of text, domain), our benchmark offers targeted insights for further research into advancing LLM causal reasoning. Our code and dataset can be found at https://github.com/Ryan-Saklad/ReCITE.
%U https://aclanthology.org/2026.acl-long.1003/
%P 21957-21989

Markdown (Informal)

[Can Large Language Models Infer Causal Relationships from Real-World Text?](https://aclanthology.org/2026.acl-long.1003/) (Saklad et al., ACL 2026)

• Can Large Language Models Infer Causal Relationships from Real-World Text? (Saklad et al., ACL 2026)

ACL

• Ryan Saklad, Aman Chadha, Oleg V. Pavlov, and Raha Moraffah. 2026. Can Large Language Models Infer Causal Relationships from Real-World Text?. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21957–21989, San Diego, California, United States. Association for Computational Linguistics.