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cs.CL2026
EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors
Amin Banayeeanzade, Qingchuan Yang, Deqing Fu +6
High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic data offers a practical substitute for do…
cs.CL2026
DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation
Saadat Hasan Khan, Spencer Hong, Jingyu Wu +4
Retrieval-augmented generation (RAG) is a common technique for grounding language model outputs in domain-specific information. However, RAG is often challenged by reasoning-intens…
cs.CL2026
Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization
Kushal Chawla, Chenyang Zhu, Pengshan Cai +9
Summarization of multi-party dialogues is a critical capability in industry, enhancing knowledge transfer and operational effectiveness across many domains. However, automatically…