6 papers
Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning
Chenlu Ding, Jiancan Wu, Yanchen Luo +3
Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit knowledge that became availab…
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…
Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation
Eamon Earl, Chen Ding, Richard Valenzano +1
In the past two decades, open access to news and information has increased rapidly, empowering educated political growth within democratic societies. News recommender systems (NRSs…
Delayed Feedback Modeling with Influence Functions
Chenlu Ding, Jiancan Wu, Yancheng Yuan +5
In online advertising under the cost-per-conversion (CPA) model, accurate conversion rate (CVR) prediction is crucial. A major challenge is delayed feedback, where conversions may…
On Negative-aware Preference Optimization for Recommendation
Chenlu Ding, Daoxuan Liu, Jiancan Wu +6
Recommendation systems leverage user interaction data to suggest relevant items while filtering out irrelevant (negative) ones. The rise of large language models (LLMs) has garnere…
Unified Parameter-Efficient Unlearning for LLMs
Chenlu Ding, Jiancan Wu, Yancheng Yuan +5
The advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks. Fin…