4 papers
OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
Haoyang Fang, Shuai Zhang, Yifei Ma +5
Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that e…
Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI
Xinhao Liu, Jiaqi Li, Youming Deng +7
Reproducible closed-loop evaluation remains a major bottleneck in Embodied AI such as visual navigation. A promising path forward is high-fidelity simulation that combines photorea…
Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms
Miaosen Zhang, Yixuan Wei, Zhen Xing +8
Modern vision models are trained on very large noisy datasets. While these models acquire strong capabilities, they may not follow the user's intent to output the desired results i…
Experimental Design for Active Transductive Inference in Large Language Models
Subhojyoti Mukherjee, Anusha Lalitha, Aniket Deshmukh +3
One emergent ability of large language models (LLMs) is that query-specific examples can be included in the prompt at inference time. In this work, we use active learning for adapt…