20 citations · 39 across the 46 of their papers we have counts for
9 papers · 1 filter
BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
Chang Zong, Hao Ning, Siliang Tang +2
Biomedical question answering often requires decisions from retrieved literature whose relevance, quality, and support for candidate answers are uneven. Most retrieval-augmented la…
CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging
Jie Cao, Zhenxuan Fan, Zhuonan Wang +8
Large language models (LLMs) achieve remarkable performance on diverse downstream and domain-specific tasks via parameter-efficient fine-tuning (PEFT). However, existing PEFT metho…
Fast Thinking for Large Language Models
Haoyu Zheng, Zhuonan Wang, Yuqian Yuan +7
Reasoning-oriented Large Language Models (LLMs) often rely on generating explicit tokens step by step, and their effectiveness typically hinges on large-scale supervised fine-tunin…
Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs
Yang Dai, Jianxiang An, Tianwei Lin +6
Multimodal Large Language Models (MLLMs) have achieved success across various domains. However, their applicability tends to degrade when confronted with different types of data in…
MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models
Jie Cao, Tianwei Lin, Bo Yuan +7
Recent studies integrate Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE) to further enhance the performance of parameter-efficient fine-tuning (PEFT) methods in Large Langu…
Chart-HQA: A Benchmark for Hypothetical Question Answering in Charts
Xiangnan Chen, Yuancheng Fang, Qian Xiao +5
Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability…
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
Tianwei Lin, Jiang Liu, Wenqiao Zhang +9
While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, especially…
Bridging Local Details and Global Context in Text-Attributed Graphs
Yaoke Wang, Yun Zhu, Wenqiao Zhang +3
Representation learning on text-attributed graphs (TAGs) is vital for real-world applications, as they combine semantic textual and contextual structural information. Research in t…
Language Model is a Branch Predictor for Simultaneous Machine Translation
Aoxiong Yin, Tianyun Zhong, Haoyuan Li +2
The primary objective of simultaneous machine translation (SiMT) is to minimize latency while preserving the quality of the final translation. Drawing inspiration from CPU branch p…