397 citations · 803 across the 63 of their papers we have counts for
8 papers · 2 filters
Data-Centric Improvements for Enhancing Multi-Modal Understanding in Spoken Conversation Modeling
Maximillian Chen, Ruoxi Sun, Sercan Ö. Arık
Conversational assistants are increasingly popular across diverse real-world applications, highlighting the need for advanced multimodal speech modeling. Speech, as a natural mode…
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
Bowen Jin, Jinsung Yoon, Jiawei Han +1
Retrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources. The increasing capacity of LLMs to process longer input sequences…
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Fei Wang, Xingchen Wan, Ruoxi Sun +2
Retrieval augmented generation (RAG), while effectively integrating external knowledge to address the inherent limitations of large language models (LLMs), can be hindered by imper…
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions
Jinsung Yoon, Raj Sinha, Sercan O Arik +1
Embeddings from Large Language Models (LLMs) have emerged as critical components in various applications, particularly for information retrieval. While high-dimensional embeddings…
BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
Hongjin Su, Howard Yen, Mengzhou Xia +12
Existing retrieval benchmarks primarily consist of information-seeking queries (e.g., aggregated questions from search engines) where keyword or semantic-based retrieval is usually…
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Yusen Zhang, Ruoxi Sun, Yanfei Chen +3
Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs). Two common strategies have emerged: 1) reducing the i…