7 papers
Reading Right, Answering Wrong: How Visual Configuration Changes Affect Evidence Use in VLMs
Dingyang Lin, Yingfeng Luo, Chenglong Wang +4
Vision-language models (VLMs) have achieved strong performance on tasks such as visual question answering, yet small image resizes can turn correct answers into errors. We investig…
Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning
Yifu Huo, Shunjie Xing, Chenglong Wang +8
Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments. A promising solution to this challenge is credit assignment, which aim…
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…
CTC-based Non-autoregressive Speech Translation
Chen Xu, Xiaoqian Liu, Xiaowen Liu +9
Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation a…
Bridging the Granularity Gap for Acoustic Modeling
Chen Xu, Yuhao Zhang, Chengbo Jiao +7
While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies…
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation
Ye Lin, Shuhan Zhou, Yanyang Li +3
For years the model performance in machine learning obeyed a power-law relationship with the model size. For the consideration of parameter efficiency, recent studies focus on incr…
On Vision Features in Multimodal Machine Translation
Bei Li, Chuanhao Lv, Zefan Zhou +4
Previous work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is on the quality of vision mode…