9 citations · 13 across the 11 of their papers we have counts for
11 papers
BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences
Ru Peng, Haokai Xu, Xijun Gu +11
While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and social sciences (HSS…
Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling
Qiyuan Chen, Hongsen Huang, Jiahe Chen +6
Vision-language reward modeling faces a dilemma: generative approaches are interpretable but slow, while discriminative ones are efficient but act as opaque "black boxes." To bridg…
Icon: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation
Qiyuan Chen, Hongsen Huang, Qian Shao +6
Large Language Models (LLMs) require high quality preference datasets to align with human preferences. However, conventional methods for constructing such datasets face significant…
CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines
Qiyuan Chen, Jiahe Chen, Hongsen Huang +7
Generative Search Engines (GSEs) synthesize conversational answers from multiple sources, weakening the long-standing link between search ranking and digital visibility. This shift…
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings
Yue Wang, Xu Cao, Yaojun Hu +7
Electrocardiogram (ECG), a non-invasive and affordable tool for cardiac monitoring, is highly sensitive in detecting acute heart attacks. However, due to the lengthy nature of ECG…
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Yue Wang, Tianfan Fu, Yinlong Xu +6
Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several…
Making Pre-trained Language Models Great on Tabular Prediction
Jiahuan Yan, Bo Zheng, Hongxia Xu +5
The transferability of deep neural networks (DNNs) has made significant progress in image and language processing. However, due to the heterogeneity among tables, such DNN bonus is…
Small Models are LLM Knowledge Triggers on Medical Tabular Prediction
Jiahuan Yan, Jintai Chen, Chaowen Hu +4
Recent development in large language models (LLMs) has demonstrated impressive domain proficiency on unstructured textual or multi-modal tasks. However, despite with intrinsic worl…
Unraveling Babel: Exploring Multilingual Activation Patterns of LLMs and Their Applications
Weize Liu, Yinlong Xu, Hongxia Xu +3
Recently, large language models (LLMs) have achieved tremendous breakthroughs in the field of NLP, but still lack understanding of their internal neuron activities when processing…
Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for Imbalanced Medical Classification
Jiahuan Yan, Haojun Gao, Zhang Kai +4
Deep learning approaches exhibit promising performances on various text tasks. However, they are still struggling on medical text classification since samples are often extremely i…
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models
Weize Liu, Guocong Li, Kai Zhang +6
Large language models (LLMs) have achieved remarkable advancements in natural language processing. However, the massive scale and computational demands of these models present form…