Bongwon Suh

As people increasingly turn to AI for personal deliberation beyond task-oriented assistance, concerns about sycophancy in these value-laden contexts have grown. Unlike human flattery, which is intentional and self-interested, AI sycophancy emerges as a byproduct of RLHF’s reward structure for use...

The transition to end-to-end Multimodal Large Language Models (MLLMs) has positioned these architectures as active social evaluators in high-stakes domains. However, it remains unclear whether these models maintain objective auditory perception or succumb to the “Hearing with Eyes” phenomenon, wh...

Recent Large Audio-Language Models (LALMs) integrate acoustic capabilities into reasoning, yet whether they reliably ground clinical judgments in audible evidence remains unproven. We introduce CliniCAST ( Clini cal C ontrolled A coustic S ynthetic T riage), a controlled benchmark that disentangl...

Multimodal Large Language Models (MLLMs) are increasingly deployed as social agents, yet their ability to integrate conflicting identity cues remains underexplored. We audit gender bias in ten recent MLLMs using a counterfactual cooperative gaming task that pairs synthetic voices with avatars of...

Generative information retrieval (GenIR) enables users to obtain synthesized information through iterative interaction with LLMs, fundamentally reshaping how AI-generated content is produced and consumed. Within this shift, users may encounter AI-generated informational content through two primar...

Conversational shopping agents powered by large language models are increasingly used for online product exploration, yet the role of interaction style in shaping shopping behavior and user experience remains underexplored in shopping IR. To address the gap, we conducted two studies in experience...