1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
RephQA: Evaluating Readability of Large Language Models in Public Health Question Answering
Weikang Qiu, Tinglin Huang, Ryan Rullo +4
Large Language Models (LLMs) hold promise in addressing complex medical problems. However, while most prior studies focus on improving accuracy and reasoning abilities, a significa…
Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI
Zheng Huang, Enpei Zhang, Weikang Qiu +7
Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to reconstruct visual stimuli, essential…
Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation
Zhichao Xu, Zongyu Wu, Yun Zhou +9
Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for domains like math and code, recent work has begun training LLMs to dynamically pla…
Mixture-of-Personas Language Models for Population Simulation
Ngoc Bui, Hieu Trung Nguyen, Shantanu Kumar +4
Advances in Large Language Models (LLMs) paved the way for their emerging applications in various domains, such as human behavior simulations, where LLMs could augment human-genera…
MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding
Weikang Qiu, Zheng Huang, Haoyu Hu +3
Decoding functional magnetic resonance imaging (fMRI) signals into text has been a key challenge in the neuroscience community, with the potential to advance brain-computer interfa…