13 papers
Fine-Grained Multi Image Object Hallucination Benchmark
Joonki Min, Chaeyun Kim, Hyungwook Choi +4
Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundam…
Safety in Batches? Understanding and Mitigating Safety Failures in Batch Prompting
Kihyun Kim, Hee-Seon Kim, Wonjun Lee +1
Batch prompting is a practical inference strategy for large language models, but its safety implications remain underexplored. We show that the success of batch prompting for utili…
PHASOR: Phase-Anchored Universal Action Representations for Humanoid Embodiments
Kihyun Kim, Chaeyun Kim, Jongho Shin +4
Learning a good action embedding space is fundamental to scalable robot policy learning, yet existing methods treat action latents as task-specific intermediates rather than first-…
Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
Kihyun Kim, Shripad Deshmukh, Nikos Vlassis +1
Inverse reinforcement learning (IRL) typically assumes demonstrations from a single optimal demonstrator, but in many applications data come from multiple imperfect demonstrators w…
STAR-Teaming: A Strategy-Response Multiplex Network Approach to Automated LLM Red Teaming
MinJae Jung, YongTaek Lim, Chaeyun Kim +3
While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. This paper introduces STAR-Team…
CAGE: A Framework for Culturally Adaptive Red-Teaming Benchmark Generation
Chaeyun Kim, YongTaek Lim, Kihyun Kim +2
Existing red-teaming benchmarks, when adapted to new languages via direct translation, fail to capture socio-technical vulnerabilities rooted in local culture and law, creating a c…
Enhanced Detection of Tiny Objects in Aerial Images
Kihyun Kim, Michalis Lazarou, Tania Stathaki
While one-stage detectors like YOLOv8 offer fast training speed, they often under-perform on detecting small objects as a trade-off. This becomes even more critical when detecting…
Beyond RLHF and NLHF: Population-Proportional Alignment under an Axiomatic Framework
Kihyun Kim, Jiawei Zhang, Asuman Ozdaglar +1
Conventional preference learning methods often prioritize opinions held more widely when aggregating preferences from multiple evaluators. This may result in policies that are bias…
Benign-to-Toxic Jailbreaking: Inducing Harmful Responses from Harmless Prompts
Hee-Seon Kim, Minbeom Kim, Wonjun Lee +2
Optimization-based jailbreaks typically adopt the Toxic-Continuation setting in large vision-language models (LVLMs), following the standard next-token prediction objective. In thi…
Towards Scalable Human-aligned Benchmark for Text-guided Image Editing
Suho Ryu, Kihyun Kim, Eugene Baek +2
A variety of text-guided image editing models have been proposed recently. However, there is no widely-accepted standard evaluation method mainly due to the subjective nature of th…
DiaTool-DPO: Multi-Turn Direct Preference Optimization for Tool-Augmented Large Language Models
Sunghee Jung, Donghun Lee, Shinbok Lee +7
Tool-Augmented Larage Language Models (TA-LLMs) have shown promise in real-world applications, but face challenges in handling incomplete queries and out-of-scope requests. While e…
A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback
Kihyun Kim, Jiawei Zhang, Asuman Ozdaglar +1
Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the und…
Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music
Hyemin Ahn, Jaehun Kim, Kihyun Kim +1
This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components:…