aiqbench analysis
This research proposes a novel framework for reconstructing latent causal graphs from text by inferring intermediate events using large language models, offering a resource-efficient alternative to methods relying on predefined observable events. The study evaluates various inference strategies, including iterative chain expansion and multi-model aggregation, against a curated database of scientifically validated causal pairs to assess validity without requiring ground-truth graphs. For practitioners, the key takeaway is the potential to leverage LLMs to map complex, implicit causal structures in unstructured text, which could enhance reasoning capabilities in downstream NLP applications.
Implicit Causal Graph Construction in Text via Chain Discovery
arXiv:2606.07525v1 Announce Type: new Abstract: Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-effect pair as the begin- and endpoint of an underlying latent causal graph and using large language models (LLMs) to infer intermediate causal events. We compare end-to-end graph construction with methods that frame the task as causal chain discovery. In the latter, graphs are built either by aggregating inferred chains or by progressively expanding partial chains through an iterative search process. We further explore Wisdom of the Crowd extensions that access causal knowledge from multiple LLMs in post-hoc aggregation and collaborative inference settings. We analyze trade-offs among these approaches and evaluate the validity of inferred causal relations using a manually curated database of 1,560 scientifically validated causal pairs. This database-based evaluation is proposed as reliable, resource-efficient, and transferable to settings where ground-truth graphs are unavailable.
Abstract:Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-effect pair as the begin- and endpoint of an underlying latent causal graph and using large language models (LLMs) to infer intermediate causal events. We compare end-to-end graph construction with methods that frame the task as causal chain discovery. In the latter, graphs are built either by aggregating inferred chains or by progressively expanding partial chains through an iterative search process. We further explore Wisdom of the Crowd extensions that access causal knowledge from multiple LLMs in post-hoc aggregation and collaborative inference settings. We analyze trade-offs among these approaches and evaluate the validity of inferred causal relations using a manually curated database of 1,560 scientifically validated causal pairs. This database-based evaluation is proposed as reliable, resource-efficient, and transferable to settings where ground-truth graphs are unavailable.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.07525 [cs.CL]
(or arXiv:2606.07525v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.07525 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 246 words
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