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 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14169–14187, Bangkok, Thailand
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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.