aiqbench analysis
This survey establishes a critical taxonomy for contextual causality in large language models, distinguishing between semantic, intervention, and counterfactual approaches to improve decision-making reliability. By mapping current research gaps between benchmarks and real-world needs, the paper outlines essential capabilities required for next-generation AI systems to accurately identify causal relations. It serves as a foundational reference for researchers aiming to advance interpretability and robustness in LLM architectures.
Contextual Causality with Large Language Models: A Survey
arXiv:2609.22409v1 Announce Type: new Abstract: Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific situations and support more reliable decision-making. Despite its significance, a systematic exploration of contextual causality with LLMs is still lacking. To fill this gap, we present a comprehensive survey on this topic. In this survey, we first propose a taxonomy of contextual causality, consisting of semantic, intervention, and counterfactual causality, and characterize each category by its core causal question, required model capabilities, representative tasks, and practical uses in causality analysis. We then analyze existing studies and discuss their key limitations. Finally, we examine the gaps between current benchmarks and real-world needs and outline promising directions for future research. Our goal is to clarify the research landscape of contextual causality with LLMs, emphasize its importance, and highlight promising future directions.
Abstract:Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific situations and support more reliable decision-making. Despite its significance, a systematic exploration of contextual causality with LLMs is still lacking. To fill this gap, we present a comprehensive survey on this topic. In this survey, we first propose a taxonomy of contextual causality, consisting of semantic, intervention, and counterfactual causality, and characterize each category by its core causal question, required model capabilities, representative tasks, and practical uses in causality analysis. We then analyze existing studies and discuss their key limitations. Finally, we examine the gaps between current benchmarks and real-world needs and outline promising directions for future research. Our goal is to clarify the research landscape of contextual causality with LLMs, emphasize its importance, and highlight promising future directions.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.22409 [cs.CL]
(or arXiv:2609.22409v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.22409 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 238 words
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