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…
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…
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)…
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…