41 citations · 60 across the 25 of their papers we have counts for
4 papers · 2 filters
DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning
Nithin Sivakumaran, Justin Chih-Yao Chen, David Wan +4
Specialized visual tools can augment large language models or vision language models with expert knowledge (e.g., grounding, spatial reasoning, medical knowledge, etc.), but knowin…
MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration
David Wan, Justin Chih-Yao Chen, Elias Stengel-Eskin +1
Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. We extend…
Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
Justin Chih-Yao Chen, Sukwon Yun, Elias Stengel-Eskin +2
Combining existing pre-trained LLMs is a promising approach for diverse reasoning tasks. However, task-level expert selection is often too coarse-grained, since different instances…
Private Text Generation by Seeding Large Language Model Prompts
Supriya Nagesh, Justin Y. Chen, Nina Mishra +1
We explore how private synthetic text can be generated by suitably prompting a large language model (LLM). This addresses a challenge for organizations like hospitals, which hold s…