18 papers
SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
Jiangnan Xia, Xuansheng Wu, Yu Yang +2
Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors. Most ex…
Less is Enough: Synthesizing Diverse Data in LLM Feature Space with Sparse Autoencoders
Zhongzhi Li, Xuansheng Wu, Yijiang Li +2
The diversity of post-training data is critical for effective downstream performance in large language models (LLMs). Many existing approaches to constructing post-training data qu…
Learnable Assessment Skills for LLM-based Automated Scoring: Rubric Construction via Iterative Optimization
Yun Wang, Xin Xia, Xuansheng Wu +2
LLM-based automated scoring approaches near-human performance, but scaling to new tasks remains bottlenecked by the per-item human configuration of upstream stages such as rubric c…
BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation
Yun Wang, Xuansheng Wu, Jingyuan Huang +3
In the field of educational assessment, automated scoring systems increasingly rely on deep learning and large language models (LLMs). However, these systems face significant risks…
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
Zhenyue Qin, Yang Liu, Yu Yin +13
Vision-threatening eye diseases pose a major global health burden, with timely diagnosis limited by workforce shortages and restricted access to specialized care. While multimodal…
Soundness-Aware Level: A Microscopic Signature that Predicts LLM Reasoning Potential
Xuansheng Wu, Xiaoman Pan, Wenlin Yao +1
Reinforcement learning with verifiable rewards (RLVR) can elicit strong reasoning in large language models (LLMs), while their performance after RLVR varies dramatically across dif…