29 citations · 134 across the 28 of their papers we have counts for
7 papers · 1 filter
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST
Shuyu Zhang, Yifan Wei, Xinru Wang +5
Zero-shot Dialog State Tracking (zs-DST) is essential for enabling Task-Oriented Dialog Systems (TODs) to generalize to new domains without costly data annotation. A central challe…
AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning
Yifan Wei, Xiaoyan Yu, Yixuan Weng +3
Large Language Models (LLMs), when enhanced through reasoning-oriented post-training, evolve into powerful Large Reasoning Models (LRMs). Tool-Integrated Reasoning (TIR) further ex…
AI Scientists Fail Without Strong Implementation Capability
Minjun Zhu, Qiujie Xie, Yixuan Weng +4
The emergence of Artificial Intelligence (AI) Scientist represents a paradigm shift in scientific discovery, with large language models (LLMs) taking the lead as the primary execut…
Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge
Bin Li, Shenxi Liu, Yixuan Weng +3
Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task has been introduced to further adv…
ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant
Yifan Xiang, Zhenxi Zhang, Bin Li +6
Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-specific concepts and generate contextual c…
DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process
Minjun Zhu, Yixuan Weng, Linyi Yang +1
Large Language Models (LLMs) are increasingly utilized in scientific research assessment, particularly in automated paper review. However, existing LLM-based review systems face si…
Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs
Haoran Fan, Bin Li, Yixuan Weng +1
While LLMs have demonstrated remarkable potential in time series forecasting, their practical deployment remains constrained by excessive computational demands and memory footprint…