4 papers
AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompters
Hanjun Luo, Zhimu Huang, Sylvia Chung +6
Text-to-image (T2I) systems increasingly rely on upstream prompters, either humans or multimodal large language models (MLLMs), to translate user intent into detailed prompts. Yet…
BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models
Hanjun Luo, Zhimu Huang, Haoyu Huang +5
Text-to-Image (T2I) generative models have revolutionized content creation, yet they inherently risk amplifying societal biases. While sociological research provides systematic cla…
PrefIx: Understand and Adapt to User Preference in Human-Agent Interaction
Jialin Li, Zhenhao Chen, Hanjun Luo +1
LLM-based agents can complete tasks correctly yet still frustrate users through poor interaction patterns, such as excessive confirmations, opaque reasoning, or misaligned pacing.…
Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition
Sneheel Sarangi, Maha Elgarf, Hanan Salam
Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others. Although this capability is crucial for human interaction, testing on Large Language M…