7 papers
Socratic Students: Teaching Language Models to Learn by Asking Questions
Rajeev Bhatt Ambati, Tianyi Niu, Aashu Singh +3
Large language Models (LLMs) are usually used to answer questions, but many high-stakes applications (e.g., tutoring, clinical support) require the complementary skill of asking qu…
RESTRAIN: From Spurious Votes to Signals -- Self-Driven RL with Self-Penalization
Zhaoning Yu, Will Su, Leitian Tao +9
Reinforcement learning with human-annotated data has boosted chain-of-thought reasoning in large reasoning models, but these gains come at high costs in labeled data while falterin…
Think Then Embed: Generative Context Improves Multimodal Embedding
Xuanming Cui, Jianpeng Cheng, Hong-you Chen +11
There is a growing interest in Universal Multimodal Embeddings (UME), where models are required to generate task-specific representations. While recent studies show that Multimodal…
StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding
Yanlai Yang, Zhuokai Zhao, Satya Narayan Shukla +4
Multimodal large language models (MLLMs) have made significant progress in visual-language reasoning, but their ability to efficiently handle long videos remains limited. Despite r…
Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
Jiang Zhang, Sumit Kumar, Wei Chang +7
The task of item-to-item (I2I) retrieval is to identify a set of relevant and highly engaging items based on a given trigger item. It is a crucial component in modern recommendatio…
Transfer between Modalities with MetaQueries
Xichen Pan, Satya Narayan Shukla, Aashu Singh +9
Unified multimodal models aim to integrate understanding (text output) and generation (pixel output), but aligning these different modalities within a single architecture often dem…