4 citations · 9 across the 17 of their papers we have counts for
13 papers · 1 filter
Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs
Zixuan Ren, Jinliang Lu, Junhong Wu +5
Model merging plays a crucial role in consolidating multiple specialized models into a single, unified model, especially in the era of large language models (LLMs). Recent research…
HTAM: Hierarchical Transition-Attended Memory for Operator Optimization
Yining Zhang, Mingyang Yi, Chen Wang +5
High-performance GPU kernels are essential for efficient LLM deployment, yet optimizing them remains expertise-intensive. Recent LLM-based code generation makes automatic GPU opera…
EmoHarbor: Evaluating Personalized Emotional Support by Simulating the User's Internal World
Jing Ye, Lu Xiang, Yaping Zhang +1
Current evaluation paradigms for emotional support conversations tend to reward generic empathetic responses, yet they fail to assess whether the support is genuinely personalized…
A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities
Lu Xiang, Yang Zhao, Yaping Zhang +1
Large Language Models (LLMs) have demonstrated their transformative potential across numerous disciplinary studies, reshaping the existing research methodologies and fostering inte…
Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
Yupu Liang, Yaping Zhang, Zhiyang Zhang +5
Multimodal Large Language Models (MLLMs) have shown strong performance in document image tasks, especially Optical Character Recognition (OCR). However, they struggle with Document…
Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation
Yupu Liang, Yaping Zhang, Zhiyang Zhang +4
Document Image Machine Translation (DIMT) aims to translate text within document images, facing generalization challenges due to limited training data and the complex interplay bet…
From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment
Jing Ye, Lu Xiang, Yaping Zhang +1
Effective emotional support hinges on understanding users' emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great…
TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification
Junnan Zhu, Min Xiao, Yining Wang +3
LLMs have achieved remarkable fluency and coherence in text generation, yet their widespread adoption has raised concerns about content reliability and accountability. In high-stak…
SimulPL: Aligning Human Preferences in Simultaneous Machine Translation
Donglei Yu, Yang Zhao, Jie Zhu +3
Simultaneous Machine Translation (SiMT) generates translations while receiving streaming source inputs. This requires the SiMT model to learn a read/write policy, deciding when to…
SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent
Jing Ye, Lu Xiang, Yaping Zhang +1
Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly form…
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation
Junhong Wu, Yang Zhao, Yangyifan Xu +2
Large Language Models (LLMs) have achieved impressive results across numerous NLP tasks but still encounter difficulties in machine translation. Traditional methods to improve tran…
Self-Modifying State Modeling for Simultaneous Machine Translation
Donglei Yu, Xiaomian Kang, Yuchen Liu +2
Simultaneous Machine Translation (SiMT) generates target outputs while receiving stream source inputs and requires a read/write policy to decide whether to wait for the next source…
F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation
Junhong Wu, Yuchen Liu, Chengqing Zong
In the evolving landscape of Neural Machine Translation (NMT), the pretrain-then-finetune paradigm has yielded impressive results. However, the persistent challenge of Catastrophic…