3 papers
cs.CL2025
Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generation
Alireza Salemi, Chris Samarinas, Hamed Zamani
This paper studies the limitations of (retrieval-augmented) large language models (LLMs) in generating diverse and comprehensive responses, and introduces the Plan-and-Refine (P&R)…
cs.IR2025
Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
Chris Samarinas, Hamed Zamani
We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimizatio…
cs.CL2025
Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text Generation
Chris Samarinas, Alexander Krubner, Alireza Salemi +2
This paper presents ICAT, an evaluation framework for measuring coverage of diverse factual information in long-form text generation. ICAT breaks down a long output text into a lis…