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cs.CL2026
BEAR: Budgeted Evidence Allocation for Multi-Document Reasoning
Lin Sun, Linglin Zhang, Jingang Huang +3
We argue that multi-document reasoning is constrained not only by how much text a model can read, but also by how limited query-time evidence budget is allocated across documents a…
cs.CL2024
DuetRAG: Collaborative Retrieval-Augmented Generation
Dian Jiao, Li Cai, Jingsheng Huang +3
Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive task…
cs.CL2023
Contextual Data Augmentation for Task-Oriented Dialog Systems
Dustin Axman, Avik Ray, Shubham Garg +1
Collection of annotated dialogs for training task-oriented dialog systems have been one of the key bottlenecks in improving current models. While dialog response generation has bee…