8 citations · 17 across the 17 of their papers we have counts for
6 papers · 1 filter
Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning
Zirui Song, Huaxing Liu, Xiang Wang +8
Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no longer expressed in their outpu…
CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding
Wei Jia, Zhicong Lu, Yu Chen +5
Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL). However, existing methods pr…
Faithful-MR1: Faithful Multimodal Reasoning via Anchoring and Reinforcing Visual Attention
Changyuan Tian, Zhicong Lu, Huaxing Liu +7
Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for advancing complex reasoning in large language models, and recent work extends RLVR to…
AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
Junfeng Fang, Houcheng Jiang, Kun Wang +5
Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates. To…
A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction
Ruihao Shui, Yixin Cao, Xiang Wang +1
Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. However, recent disputes over GPT-4's law evaluation raise…
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
Zhiyuan Liu, Sihang Li, Yanchen Luo +5
Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception - a critical a…