Papers with Learning-to-Rank
LiPO: Listwise Preference Optimization through Learning-to-Rank (2025.naacl-long)
Copied to clipboard
Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J Liu, Xuanhui Wang
| Challenge: | Recent work on language models with curated feedback provides promising alternatives to RLHF . multiple responses can be ranked by reward models or AI feedback, but there is no study on directly fitting upon a list of responses. |
| Approach: | They propose a method that aligns language models with curated human feedback . they propose SLiC and DPO as promising alternatives to traditional RLHF . |
| Outcome: | The proposed method outperforms DPO and SLiC on several preference alignment tasks with curated and real rankwise preference data. |
Learning to Rank Salient Content for Query-focused Summarization (2024.emnlp-main)
Copied to clipboard
| Challenge: | Query-focused summarization (QFS) is gaining prominence in research community. |
| Approach: | They propose to integrate Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization. |
| Outcome: | The proposed model outperforms the state-of-the-art on QMSum benchmark and SQuALITY benchmark while offering a lower training overhead. |