6 citations · 19 across the 15 of their papers we have counts for
6 papers · 1 filter
MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation
Xiaoyu Kong, Leheng Sheng, Junfei Tan +5
The recent success of large language models (LLMs) has renewed interest in whether recommender systems can achieve similar scaling benefits. Conventional recommenders, dominated by…
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…
AlphaAlign: Incentivizing Safety Alignment with Extremely Simplified Reinforcement Learning
Yi Zhang, An Zhang, XiuYu Zhang +4
Large language models (LLMs), despite possessing latent safety understanding from their vast pretraining data, remain vulnerable to generating harmful content and exhibit issues su…
On Reasoning Strength Planning in Large Reasoning Models
Leheng Sheng, An Zhang, Zijian Wu +5
Recent studies empirically reveal that large reasoning models (LRMs) can automatically allocate more reasoning strengths (i.e., the number of reasoning tokens) for harder problems,…
AlphaSteer: Learning Refusal Steering with Principled Null-Space Constraint
Leheng Sheng, Changshuo Shen, Weixiang Zhao +6
As LLMs are increasingly deployed in real-world applications, ensuring their ability to refuse malicious prompts, especially jailbreak attacks, is essential for safe and reliable u…
AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
Yu Shang, Peijie Liu, Yuwei Yan +9
The emergence of agentic recommender systems powered by Large Language Models (LLMs) represents a paradigm shift in personalized recommendations, leveraging LLMs' advanced reasonin…