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Xiangxu Zhang

2 accepted papers

2026

MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions

AAAI 2026technical

Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despite substantial work investigating the trustworthiness of language models, MMLMs

Cited by 0SourcePDFScholar
2025

AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels

EMNLP 2025

Medical information retrieval (MIR) is vital for accessing knowledge from electronic health records, scientific literature, and medical databases, supporting applications such as medical education, patient queries, and clinical diagnosis. However, effective zero-shot dense retrieval in the medical d