1 citations · 1 across the 9 of their papers we have counts for
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From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship
Yue Xu, Wenjie Wang
Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in socially sensitive domains. Existing benchmark…
DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Decoupled Reasoning
Hongye Qiu, Yue Xu, Yi Wang +2
Large Language Models (LLMs) exhibit strong natural language understanding capabilities but also inherit and amplify societal biases, particularly gender bias, raising fairness con…
Auto-Search and Refinement: An Automated Framework for Gender Bias Mitigation in Large Language Models
Yue Xu, Chengyan Fu, Li Xiong +2
Pre-training large language models (LLMs) on vast text corpora enhances natural language processing capabilities but risks encoding social biases, particularly gender bias. While p…
Cross-modality Information Check for Detecting Jailbreaking in Multimodal Large Language Models
Yue Xu, Xiuyuan Qi, Zhan Qin +1
Multimodal Large Language Models (MLLMs) extend the capacity of LLMs to understand multimodal information comprehensively, achieving remarkable performance in many vision-centric t…
: Natural and Universal Adversarial Attacks on Prompt-based Language Models
Yue Xu, Wenjie Wang
Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks…