13 papers
Document-as-Image Representations Fall Short for Scientific Retrieval
Ghazal Khalighinejad, Raghuveer Thirukovalluru, Alexander H. Oh +1
Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying source. Meanwhile, existing be…
Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models
Yukun Huang, Sanxing Chen, Jian Pei +2
Trustworthy language models should provide both correct and verifiable answers. However, citations generated directly by standalone LLMs are often unreliable. As a result, current…
Coding Agents are Effective Long-Context Processors
Weili Cao, Xunjian Yin, Bhuwan Dhingra +1
Large Language Models (LLMs) have demonstrated remarkable progress in scaling to access massive contexts. However, the access is via the latent and uninterpretable attention mechan…
Text-Guided Semantic Image Encoder
Raghuveer Thirukovalluru, Xiaochuang Han, Bhuwan Dhingra +2
Image encoders, a fundamental component of vision-language models (VLMs), are typically pretrained independently before being aligned with a language model. This standard paradigm…
InData: Towards Secure Multi-Step, Tool-Based Data Analysis
Karthikeyan K, Raghuveer Thirukovalluru, Bhuwan Dhingra +1
Large language model agents for data analysis typically generate and execute code directly on databases. However, when applied to sensitive data, this approach poses significant se…
Breaking the Batch Barrier (B3) of Contrastive Learning via Smart Batch Mining
Raghuveer Thirukovalluru, Rui Meng, Ye Liu +7
Contrastive learning (CL) is a prevalent technique for training embedding models, which pulls semantically similar examples (positives) closer in the representation space while pus…