13 papers
SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs
Jie Sun, Yu Liu, Lu Han +9
While transformer-based Large Language Models (LLMs) theoretically support massive context windows, they suffer from severe performance degradation when processing long numerical s…
On the Direction of RLVR Updates for LLM Reasoning: Identification and Exploitation
Kexin Huang, Haoming Meng, Junkang Wu +10
Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models. While existing analyses identify that RLVR-ind…
MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering
Chenlu Ding, Jiancan Wu, Leheng Sheng +4
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities across vision-language tasks, yet their large-scale deployment raises pressing concerns about mem…
On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
Wenyu Mao, Jiancan Wu, Guoqing Hu +3
Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories…
Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning
Yutian Liu, Zhengyi Yang, Jiancan Wu +1
Recent advances have applied large language models (LLMs) to sequential recommendation, leveraging their pre-training knowledge and reasoning capabilities to provide more personali…
Multi-Grained Patch Training for Efficient LLM-based Recommendation
Jiayi Liao, Ruobing Xie, Sihang Li +4
Large Language Models (LLMs) have emerged as a new paradigm for recommendation by converting interacted item history into language modeling. However, constrained by the limited con…