8 citations · 9 across the 6 of their papers we have counts for
6 papers
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)…
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…
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…
ProCIS: A Benchmark for Proactive Retrieval in Conversations
Chris Samarinas, Hamed Zamani
The field of conversational information seeking, which is rapidly gaining interest in both academia and industry, is changing how we interact with search engines through natural la…
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…