8 citations · 9 across the 6 of their papers we have counts for
4 papers · 1 filter
Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning
Chris Samarinas, Haw-Shiuan Chang, Hamed Zamani
Reinforcement learning has emerged as an effective paradigm for training large language models to interleave reasoning with search engine calls. However, existing approaches face a…
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)…
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…
Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
Chris Samarinas, Pracha Promthaw, Atharva Nijasure +3
This paper explores SynTOD, a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling complex tasks such as intent…