A systematic survey that analyzes 134 papers, proposes a unified taxonomy for evidence-based LLM text generation, and examines 300 evaluation metrics across seven dimensions.
In Proceed- ings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’24, page 1211–1228, New York, NY , USA
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Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models
A systematic survey that analyzes 134 papers, proposes a unified taxonomy for evidence-based LLM text generation, and examines 300 evaluation metrics across seven dimensions.