2 citations · 5 across the 24 of their papers we have counts for
4 papers · 1 filter
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin
Hao Yi, Qingyang Li, Yulan Hu +3
Recently, enhancing the numerical and logical reasoning capability of Large Language Models (LLMs) has emerged as a research hotspot. Existing methods face several limitations: inf…
VidCapBench: A Comprehensive Benchmark of Video Captioning for Controllable Text-to-Video Generation
Xinlong Chen, Yuanxing Zhang, Chongling Rao +7
The training of controllable text-to-video (T2V) models relies heavily on the alignment between videos and captions, yet little existing research connects video caption evaluation…
Towards Comprehensive Preference Data Collection for Reward Modeling
Yulan Hu, Qingyang Li, Sheng Ouyang +6
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models (LLMs) with human preferences, thereby enhancing the quality of responses gener…
Chain-of-Specificity: An Iteratively Refining Method for Eliciting Knowledge from Large Language Models
Kaiwen Wei, Jingyuan Zhang, Hongzhi Zhang +4
Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that L…