Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey

arXiv:2403.09606v1 Announce Type: new
Abstract: 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 …

abstract accuracy advanced arxiv causal causal inference collaboration cs.ai cs.cl domains emergence explainability fairness generative inference language language models language processing large language large language models llms natural natural language natural language processing nlp predictive processing relationships robustness survey through type variables