Sketch-Guided Constrained Decoding for Boosting Blackbox Large Language Models without Logit Access
Comments Accepted to ACL 2024 Oral
期刊&会议
Annual Meeting of the Association for Computational Linguistics · 会议 · Natural Language Processing
Comments Accepted to ACL 2024 Oral
Comments 46 pages, 23 figures, 45 tables. Accepted in ACL 2024 findings
Comments Published in ACL 2024
Comments Accepted as a long paper at ACL 2022
Comments ACL 2024 Main Conference
Comments Accepted to ACL 2024 Student Research Workshop (ACL-SRW 2024)
Comments Accepted by ACL 2024 Main Conference
Journal ref ACL Findings 2024
Comments Accepted by ACL2024 (Main)
Comments ACL 2024 Student Research Workshop
Comments Accepted to the ACL PrivateNLP 2024 Workshop, 7 pages, 2 figures
Comments Accepted by ArabicNLP 2024, presented in ACL 2024
Comments In Proceedings of The First SIGTURK workshop co-located with ACL 2024: https://sigturk.github.io/workshop/
Comments ACL 2024 Main
Comments Accepted to ACL 2024
Comments Accepted to ACL 2024 Findings; Project Page: https://hechang25.github.io/MS2SL
Comments Findings of ACL 2024
Comments Machine Learning for Ancient Languages, ACL 2024 Workshop, 15 August 2024
Comments Short Paper, ACL 2024
Comments ACL 2024 Main Conference
Comments Accepted at ACL 2024 - Proceedings of the 62st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Journal ref Proceedings of the 62st Annual Meeting of the Association for Computational Linguistics - Volume 1: Long Papers (ACL 2024)
Comments Accepted to ACL 2024 Main Conference
Comments 16 pages, 5 figures; accepted by the main conference of ACL2023
Comments Accepted at EACL 2023
Journal ref Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. (2023) 2799-2829
Comments Accepted to ACL 2023
Comments ACL2024 submitted
Comments 18 pages, 3 figures; Accepted by ACL-2024
Comments Published at ACL 2023
Journal ref ACL 2023
Comments Accepted in Findings of ACL 2024
Comments Accepted to ACL 2024. Code is available at https://github.com/amazon-science/learning-to-generate-answers-with-citations