1 citations · 1 across the 4 of their papers we have counts for
4 papers
Same Semantics, Different Outcome: On the Modality Robustness of Multimodal LLMs under Knowledge Conflict
Jungyeon Lee, Yejin Yoon, Taeuk Kim
Multimodal large language models (MLLMs) are increasingly provided with contextual evidence in heterogeneous forms: as a text passage, as a rendered image of the same passage, or a…
OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models
Seunghee Kim, Bumkyu Park, Kyudan Jung +5
Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we…
MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation
Jungyeon Lee, Kangmin Lee, Taeuk Kim
Knowledge conflict often arises in retrieval-augmented generation (RAG) systems, where retrieved documents may be inconsistent with one another or contradict the model's parametric…
BlendX: Complex Multi-Intent Detection with Blended Patterns
Yejin Yoon, Jungyeon Lee, Kangsan Kim +2
Task-oriented dialogue (TOD) systems are commonly designed with the presumption that each utterance represents a single intent. However, this assumption may not accurately reflect…