10 papers
Multimodal Fact-Level Attribution for Verifiable Reasoning
David Wan, Han Wang, Ziyang Wang +3
Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation, where reliability requires grounding mo…
MERRIN: A Benchmark for Multimodal Evidence Retrieval and Reasoning in Noisy Web Environments
Han Wang, David Wan, Hyunji Lee +6
Motivated by the underspecified, multi-hop nature of search queries and the multimodal, heterogeneous, and often conflicting nature of real-world web results, we introduce MERRIN (…
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…
QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization
Shiyue Zhang, David Wan, Arie Cattan +3
How to properly conduct human evaluations for text summarization is a longstanding challenge. The Pyramid human evaluation protocol, which assesses content selection by breaking th…
Localizing Factual Inconsistencies in Attributable Text Generation
Arie Cattan, Paul Roit, Shiyue Zhang +5
There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existin…