4 citations · 6 across the 6 of their papers we have counts for
7 papers
Mitigating Video Hallucinations via Attention-Guided Representation Perturbation
Zhixin Cai, Ruoqi Yuan, Xinchen Yang +4
Video large language models (Video LLMs) often generate responses unsupported by the input video. Contrastive decoding (CD) can mitigate these hallucinations, but its effectiveness…
CircuitLens: Reasoning Circuits as Data Selection Signals for Reinforcement Learning with Verifiable Rewards
Zhuofan Chen, Ziqian Jiao, Yikai Cui +3
Reinforcement learning with verifiable rewards (RLVR) is sensitive to which problems a model trains on, yet existing selection criteria--difficulty filtering, hand-curation, reward…
Xetrieval: Explaining Dense Retrieval through Feature Elicitation
Zhixin Cai, Jun Bai, Yang Liu +7
Dense retrieval enables effective semantic matching, yet the semantic factors underlying its relevance scores remain opaque. Sparse autoencoders (SAEs) support feature-level explan…
Your Dense Retriever is Secretly an Expeditious Reasoner
Yichi Zhang, Jun Bai, Zhixin Cai +4
Dense retrievers enhance retrieval by encoding queries and documents into continuous vectors, but they often struggle with reasoning-intensive queries. Although Large Language Mode…
CogAtom: From Cognitive Atoms to Olympiad-level Mathematical Reasoning in Large Language Models
Zhuofan Chen, Jiyuan He, Yichi Zhang +4
Mathematical reasoning poses significant challenges for Large Language Models (LLMs) due to its demand for multi-step reasoning and abstract conceptual integration. While recent te…
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking
Jun Bai, Zhuofan Chen, Zhenzi Li +5
Text ranking has witnessed significant advancements, attributed to the utilization of dual-encoder enhanced by Pre-trained Language Models (PLMs). Given the proliferation of availa…
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Zhang Xiong, Haoxuan Li, Zhuang Liu +4
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness…