6 papers
The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers
Zhe Xu, Prachi Agrawal, Kavosh Asadi +17
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains b…
Tokenizing Numerical and Embedding Features for LLM RecSys
Zhe Xu, Ankit Peshin, Chiyu Zhang +7
Large language models (LLMs) are increasingly used as backbone architectures for recommender systems because of their strong sequence modeling and representation learning capabilit…
Selective LoRA for Visual Tokens and Attention Heads
Tiange Luo, Lajanugen Logeswaran, Jaekyeom Kim +2
Low-rank adaptation (LoRA) is widely used for parameter-efficient fine-tuning, but its standard all-token, all-head design ignores the heterogeneous structure of vision language mo…
View Selection for 3D Captioning via Diffusion Ranking
Tiange Luo, Justin Johnson, Honglak Lee
Scalable annotation approaches are crucial for constructing extensive 3D-text datasets, facilitating a broader range of applications. However, existing methods sometimes lead to th…
Visual Test-time Scaling for GUI Agent Grounding
Tiange Luo, Lajanugen Logeswaran, Justin Johnson +1
We introduce RegionFocus, a visual test-time scaling approach for Vision Language Model Agents. Understanding webpages is challenging due to the visual complexity of GUI images and…
Probing Visual Language Priors in VLMs
Tiange Luo, Ang Cao, Gunhee Lee +2
Despite recent advances in Vision-Language Models (VLMs), they may over-rely on visual language priors existing in their training data rather than true visual reasoning. To investi…