1 citations · 6 across the 21 of their papers we have counts for
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LRHP: Learning Representations for Human Preferences via Preference Pairs
Chenglong Wang, Yang Gan, Yifu Huo +7
To improve human-preference alignment training, current research has developed numerous preference datasets consisting of preference pairs labeled as "preferred" or "dispreferred".…
RoVRM: A Robust Visual Reward Model Optimized via Auxiliary Textual Preference Data
Chenglong Wang, Yang Gan, Yifu Huo +9
Large vision-language models (LVLMs) often fail to align with human preferences, leading to issues like generating misleading content without proper visual context (also known as h…
Cross-layer Attention Sharing for Pre-trained Large Language Models
Yongyu Mu, Yuzhang Wu, Yuchun Fan +9
To enhance the efficiency of the attention mechanism within large language models (LLMs), previous works primarily compress the KV cache or group attention heads, while largely ove…
Translate-and-Revise: Boosting Large Language Models for Constrained Translation
Pengcheng Huang, Yongyu Mu, Yuzhang Wu +4
Imposing constraints on machine translation systems presents a challenging issue because these systems are not trained to make use of constraints in generating adequate, fluent tra…
Hybrid Alignment Training for Large Language Models
Chenglong Wang, Hang Zhou, Kaiyan Chang +5
Alignment training is crucial for enabling large language models (LLMs) to cater to human intentions and preferences. It is typically performed based on two stages with different o…
Revealing the Parallel Multilingual Learning within Large Language Models
Yongyu Mu, Peinan Feng, Zhiquan Cao +8
In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel In…