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 2024 Conference on Empir- ical Methods in Natural Language Processing , pages 17226–17239, Miami, Florida, USA
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
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.