1 citations · 1 across the 5 of their papers we have counts for
7 papers
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…
PrefixNLI: Detecting Factual Inconsistencies as Soon as They Arise
Sapir Harary, Eran Hirsch, Aviv Slobodkin +3
Natural Language Inference (NLI) models have been used in various ways to improve the factuality of LLM outputs. This is typically done by applying an NLI model to judge whether th…
GenerationPrograms: Fine-grained Attribution with Executable Programs
David Wan, Eran Hirsch, Elias Stengel-Eskin +2
Recent large language models (LLMs) achieve impressive performance in source-conditioned text generation but often fail to correctly provide fine-grained attributions for their out…
CLaMR: Contextualized Late-Interaction for Multimodal Content Retrieval
David Wan, Han Wang, Elias Stengel-Eskin +2
Online video web content is richly multimodal: a single video blends vision, speech, ambient audio, and on-screen text. Retrieval systems typically treat these modalities as indepe…
LAQuer: Localized Attribution Queries in Content-grounded Generation
Eran Hirsch, Aviv Slobodkin, David Wan +3
Grounded text generation models often produce content that deviates from their source material, requiring user verification to ensure accuracy. Existing attribution methods associa…
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…