Learning Personalized Alignment for Evaluating Open-ended Text Generation
Open Access
- 1 January 2024
- conference paper
- Published by Association for Computational Linguistics (ACL)
- p. 13274-13292
Abstract
Danqing Wang, Kevin Yang, Hanlin Zhu, Xiaomeng Yang, Andrew Cohen, Lei Li, Yuandong Tian. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.Keywords
All Related Versions
This publication has 22 references indexed in Scilit:
- When large language models meet personalization: perspectives of challenges and opportunitiesWorld Wide Web, 2024
- Refocusing on Relevance: Personalization in NLG2021
- The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation2021
- COMET: A Neural Framework for MT Evaluation2020
- Narrative Text Generation with a Latent Discrete Plan2020
- UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation2020
- Multi-view Story Characterization from Movie Plot Synopses and Reviews2020
- BLEURT: Learning Robust Metrics for Text Generation2020
- Returning the N to NLP: Towards Contextually Personalized Classification Models2020
- Matrix Factorization Techniques for Recommender SystemsComputer, 2009