26 citations · 42 across the 10 of their papers we have counts for
11 papers
Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning
Wei Fang, James Glass
LLM agents operating over massive, dynamic tool libraries rely on effective retrieval, yet standard single-shot dense retrievers struggle with complex requests. These failures prim…
MedDialogRubrics: A Comprehensive Benchmark and Evaluation Framework for Multi-turn Medical Consultations in Large Language Models
Lecheng Gong, Weimin Fang, Ting Yang +9
Medical conversational AI (AI) plays a pivotal role in the development of safer and more effective medical dialogue systems. However, existing benchmarks and evaluation frameworks…
Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models
Meidan Ding, Jipeng Zhang, Wenxuan Wang +6
Multimodal large language models (MLLMs) hold significant potential in medical applications, including disease diagnosis and clinical decision-making. However, these tasks require…
Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space
Wei Fang, Priyadarshini Panda
Neuromorphic event cameras possess superior temporal resolution, power efficiency, and dynamic range compared to traditional cameras. However, their asynchronous and sparse data fo…
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
Jian Hu, Xibin Wu, Wei Shen +12
Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the al…
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
Yung-Sung Chuang, Wei Fang, Shang-Wen Li +2
We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering. EAR first applies a query expansio…
Interpretable Unified Language Checking
Tianhua Zhang, Hongyin Luo, Yung-Sung Chuang +7
Despite recent concerns about undesirable behaviors generated by large language models (LLMs), including non-factual, biased, and hateful language, we find LLMs are inherent multi-…
Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models
Wei Fang, Yu-An Chung, James Glass
Modern text-to-speech (TTS) systems are able to generate audio that sounds almost as natural as human speech. However, the bar of developing high-quality TTS systems remains high s…
FAKTA: An Automatic End-to-End Fact Checking System
Moin Nadeem, Wei Fang, Brian Xu +2
We present FAKTA which is a unified framework that integrates various components of a fact checking process: document retrieval from media sources with various types of reliability…
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh +1
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input insta…
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
Wei Fang, Jui-Yang Hsu, Hung-yi Lee +1
Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a r…