Inho Kang

Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existing works primarily optimize for relevance while often overlooking document trustworthiness. This is critical in high-...

Model merging has emerged as an effective approach for integrating multiple task-specific fine-tuned models into a single unified model without requiring additional data-intensive training. A central challenge in model merging is to reduce task interference while preserving the task-specific capa...

Masked diffusion language models (MDLMs) enable efficient parallel decoding but are limited by a monotonic unmasking policy, where committed tokens cannot be revised. While remasking-based methods mitigate early errors, they mainly intervene during generation. In this work, we study post-hoc refi...