3 citations · 6 across the 10 of their papers we have counts for
10 papers
DocReward: A Document Reward Model for Structuring and Stylizing
Junpeng Liu, Yuzhong Zhao, Bowen Cao +17
Recent agentic workflows automate professional document generation but focus narrowly on textual quality, overlooking structural and stylistic professionalism, which is equally cri…
AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting
Shijue Huang, Hongru Wang, Wanjun Zhong +4
Modern large reasoning models demonstrate impressive problem-solving capabilities by employing sophisticated reasoning strategies. However, they often struggle to balance efficienc…
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Deng Cai, Huayang Li, Tingchen Fu +11
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications. In this paper, we demonstra…
On the Worst Prompt Performance of Large Language Models
Bowen Cao, Deng Cai, Zhisong Zhang +2
The performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts, which raises significant concerns about their reliability in real-world scenarios.…
Retrieval is Accurate Generation
Bowen Cao, Deng Cai, Leyang Cui +4
Standard language models generate text by selecting tokens from a fixed, finite, and standalone vocabulary. We introduce a novel method that selects context-aware phrases from a co…
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Xuxin Cheng, Bowen Cao, Qichen Ye +3
Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impa…
NADiffuSE: Noise-aware Diffusion-based Model for Speech Enhancement
Wen Wang, Dongchao Yang, Qichen Ye +2
The goal of speech enhancement (SE) is to eliminate the background interference from the noisy speech signal. Generative models such as diffusion models (DM) have been applied to t…
Alleviating Over-smoothing for Unsupervised Sentence Representation
Nuo Chen, Linjun Shou, Ming Gong +5
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language mo…
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning
Bowen Cao, Qichen Ye, Weiyuan Xu +1
Learning representations for graph-structured data is essential for graph analytical tasks. While remarkable progress has been made on static graphs, researches on temporal graphs…
FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
Qichen Ye, Bowen Cao, Nuo Chen +2
Knowledge-aware question answering (KAQA) requires the model to answer questions over a knowledge base, which is essential for both open-domain QA and domain-specific QA, especiall…