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Yonghyun Jun

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IHDec: Divergence-Steered Contrastive Decoding for Securing Multi-Turn Instruction Hierarchies

IHDec (Instruction Hierarchy-steered Decoding) leverages JSD to automatically detect token-level hierarchy violations and dynamically executes contrastive decoding to suppress misaligned subordinate roles, and outperforms training-based baselines in multi-turn conflicts while fully preserving general response quality.

Nicole Geumheon Liu, Haeun Jang, Yonghyun Jun et al. · 0 citations
#natural language process... Open access Sep 2026

Leveraging fine-grained error correction in Korean speech recognition for consultation services

This work introduces DasanCallDial, the first large-scale Korean benchmark dataset specifically curated for dialogue-level ASR error correction, and proposes Detector-Gated Contextual Span Correction (DCSC), a text-only post-editing framework for error-sparse Korean speech recognition transcripts.

Yonghyun Jun, Ji-Min Lee, Hwan Chang et al. · 0 citations
#natural language process... Preprint Jan 2026

Identifying and Mitigating Bottlenecks in Role-Playing Agents: A Systematic Study of Disentangling Character Profile Axes

Field-Aware Contrastive Decoding (FACD) is proposed, a training-free strategy that amplifies suppressed disposition-sensitive signals, significantly closing the performance gap without sacrificing moral-character performance.

Yonghyun Jun, Junhyuk Choi, Jihyeon Park et al. · 1 citation

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