1 citations · 2 across the 7 of their papers we have counts for
8 papers
Probing Preference Representations: A Multi-Dimensional Evaluation and Analysis Method for Reward Models
Chenglong Wang, Yifu Huo, Yang Gan +10
Previous methods evaluate reward models by testing them on a fixed pairwise ranking test set, but they typically do not provide performance information on each preference dimension…
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang +9
Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains chall…
MRO: Enhancing Reasoning in Diffusion Language Models via Multi-Reward Optimization
Chenglong Wang, Yang Gan, Hang Zhou +10
Recent advances in diffusion language models (DLMs) have presented a promising alternative to traditional autoregressive large language models (LLMs). However, DLMs still lag behin…
GRAM-R: Self-Training Generative Foundation Reward Models for Reward Reasoning
Chenglong Wang, Yongyu Mu, Hang Zhou +10
Significant progress in reward modeling over recent years has been driven by a paradigm shift from task-specific designs towards generalist reward models. Despite this trend, devel…
GRAM: A Generative Foundation Reward Model for Reward Generalization
Chenglong Wang, Yang Gan, Yifu Huo +8
In aligning large language models (LLMs), reward models have played an important role, but are standardly trained as discriminative models and rely only on labeled human preference…
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…