Papers with statistical correlation

2 papers
Counterfactual reasoning: Testing language models’ understanding of hypothetical scenarios (2023.acl-short)

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Challenge: Existing language models have demonstrated remarkable improvements in downstream tasks, but it remains unclear to what extent they are consequences of correlation with linguistic heuristics versus robust reasoning about causal relations grounded on understanding of world knowledge.
Approach: They propose to test language models with counterfactual conditionals to test their ability to distinguish hypothetical scenarios from reality.
Outcome: The proposed model overrides real-world knowledge in counterfactual scenarios, but most models are driven by lexical cues.
iTAG: Inverse Design for Natural Text Generation with Accurate Causal Graph Annotations (2026.acl-long)

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Challenge: Lack of causally annotated text data for use as ground truth hinders causal discovery . early template-based generation methods sacrifice text naturalness in exchange for high annotation costs .
Approach: They propose a method which performs real-world concept assignment to nodes before converting causal graphs into text.
Outcome: The proposed method shows high annotation accuracy and naturalness across extensive tests.

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