6 citations · 14 across the 15 of their papers we have counts for
6 papers · 1 filter
VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding
Jian Chen, Ming Li, Jihyung Kil +6
Most organizational data in this world are stored as documents, and visual retrieval plays a crucial role in unlocking the collective intelligence from all these documents. However…
DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer
Ruoyu Wang, Junda Wu, Yu Xia +4
Large language model-based agents, empowered by in-context learning (ICL), have demonstrated strong capabilities in complex reasoning and tool-use tasks. However, existing works ha…
A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
Zhouhang Xie, Junda Wu, Yiran Shen +9
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the a…
In-context Ranking Preference Optimization
Junda Wu, Rohan Surana, Zhouhang Xie +6
Recent developments in Direct Preference Optimization (DPO) allow large language models (LLMs) to function as implicit ranking models by maximizing the margin between preferred and…
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
Chengkai Huang, Junda Wu, Yu Xia +9
Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems that extend beyond the capabilities of standalone models. By empowering LLMs to…
Active Learning for Direct Preference Optimization
Branislav Kveton, Xintong Li, Julian McAuley +4
Direct preference optimization (DPO) is a form of reinforcement learning from human feedback (RLHF) where the policy is learned directly from preferential feedback. Although many m…