5 papers
DiscussLLM: Teaching Large Language Models When to Speak
Deep Anil Patel, Iain Melvin, Christopher Malon +1
Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, yet they largely operate as reactive agents, responding only…
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
Xiaoxiao He, Quan Dao, Ligong Han +14
Discrete diffusion models have achieved success in tasks like image generation and masked language modeling but face limitations in controlled content editing. We introduce DICE (D…
EditGRPO: Reinforcement Learning with Post-Rollout Edits for Clinically Accurate Chest X-Ray Report Generation
Kai Zhang, Christopher Malon, Lichao Sun +1
Radiology report generation requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. Although recent innovations, particularly multimod…
Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation
Yun-Wei Chu, Kai Zhang, Christopher Malon +1
Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challenge, especially in fields like hea…
Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models
Zi'ou Zheng, Christopher Malon, Martin Renqiang Min +1
When performing complex multi-step reasoning tasks, the ability of Large Language Models (LLMs) to derive structured intermediate proof steps is important for ensuring that the mod…