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Large Language Models and Causal Inference in Collaboration: A Survey
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Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables. The emergence of generative Large Language Models (LLMs) has significantly impacted various NLP domains, particularly through their advanced reasoning capabilities. This survey focuses on evaluating and improving LLMs from a causal view in the following areas: understanding and improving the LLMs' reasoning capacity, addressing fairness and safety issues in LLMs, complementing LLMs with explanations, and handling multimodality. Meanwhile, LLMs' strong reasoning capacities can in turn contribute to the field of causal inference by aiding causal relationship discovery and causal effect estimations. This review explores the interplay between causal inference frameworks and LLMs from both perspectives, emphasizing their collective potential to further the development of more advanced and equitable artificial intelligence systems.
Forward citations
Cited by 4 Pith papers
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Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
LLMs perform much worse on causal questions built from post-cutoff news articles, suggesting their apparent causal skill is mostly memorization, and a general-knowledge prompt method only partly closes the gap.
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Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
LLM explanations split into local and mechanistic tracks; the paper argues they are trustworthy only if they pass causal and contrastive stress tests, adapt to the explainee, and satisfy eight trust principles.
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Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
AI can be used to generate interactive signal processing courseware, but the paper offers no evidence that students learn better from it.
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