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 Proceedings of the 2023 Con- ference on Empirical Methods in Natural Lan- guage Processing, pages 12076–12100, Singa- pore
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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.