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Belief Cascades Drive Persuasion in LLM Agent Networks
Haoyi Qiu, Genglin Liu, Pranav Narayanan Venkit +4
Multi-agent LLM systems increasingly debate answers, coordinate research, simulate users, and mediate information flows, making agent-to-agent persuasion a basic but undermeasured…
MMGR: Multi-Modal Generative Reasoning Benchmark and Evaluation
Zefan Cai, Haoyi Qiu, Tianyi Ma +13
Modern multimodal generative models can synthesize visually compelling images and videos, but it remains unclear whether this visual fluency reflects genuine reasoning: when prompt…
Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion
Haoyi Qiu, Yilun Zhou, Pranav Narayanan Venkit +4
As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored the dynamics of Agent-to-Agent (A2…
From Preferences to Prejudice: The Role of Alignment Tuning in Shaping Social Bias in Video Diffusion Models
Zefan Cai, Haoyi Qiu, Haozhe Zhao +6
Recent advances in video diffusion models have significantly enhanced text-to-video generation, particularly through alignment tuning using reward models trained on human preferenc…
Multimodal Cultural Safety: Evaluation Framework and Alignment Strategies
Haoyi Qiu, Kung-Hsiang Huang, Ruichen Zheng +2
Large vision-language models (LVLMs) are increasingly deployed in globally distributed applications, such as tourism assistants, yet their ability to produce culturally appropriate…
Evaluating Cultural and Social Awareness of LLM Web Agents
Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal +4
As large language models (LLMs) expand into performing as agents for real-world applications beyond traditional NLP tasks, evaluating their robustness becomes increasingly importan…
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
Haoyi Qiu, Wenbo Hu, Zi-Yi Dou +1
Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs, undermining their reliability…
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-Driven Negative Samples Generation
Haoyi Qiu, Kung-Hsiang Huang, Jingnong Qu +1
Ensuring factual consistency is crucial for natural language generation tasks, particularly in abstractive summarization, where preserving the integrity of information is paramount…
Gender Biases in Automatic Evaluation Metrics for Image Captioning
Haoyi Qiu, Zi-Yi Dou, Tianlu Wang +2
Model-based evaluation metrics (e.g., CLIPScore and GPTScore) have demonstrated decent correlations with human judgments in various language generation tasks. However, their impact…