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Zooming in on zero-shot intent-guided and grounded document generation using LLMs. In Proceedings of the 17th international natural language generation conference (pp. 676\u2013694).","DOI":"10.18653\/v1\/2024.inlg-main.52"},{"key":"10.1016\/j.ipm.2026.104922_b101","series-title":"HALoGEN: Fantastic LLM hallucinations and where to find them","author":"Ravichander","year":"2025"},{"key":"10.1016\/j.ipm.2026.104922_b102","doi-asserted-by":"crossref","unstructured":"Rawte, V., Chakraborty, S., Pathak, A., Sarkar, A., Tonmoy, S., Chadha, A., Sheth, A., & Das, A (2023). The troubling emergence of hallucination in large language models - An extensive definition, quantification, and prescriptive remediations. In Proceedings of the 2023 conference on empirical methods in natural language processing (pp. 2541\u20132573).","DOI":"10.18653\/v1\/2023.emnlp-main.155"},{"key":"10.1016\/j.ipm.2026.104922_b103","doi-asserted-by":"crossref","unstructured":"Reiter, E. (1994). 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