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Korea Telecom

Industry researchasia · kr
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Research library54linked papers
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Selected work

Representative Papers

Mi:dm 2.0 Korea-centric Bilingual Language Models

Jan 14, 2026

This work addresses the limitations of current large language models in handling Korean, which stem from low-quality training data and a lack of cultural alignment, hindering their ability to capture Korea-specific values, commonsense knowledge, and nuanced emotional expressions. To overcome these challenges, we propose Mi:dm 2.0—the first bilingual large language model systematically integrating Korean sociocultural commonsense and reasoning patterns. Through high-quality data curation, synthetic data generation, a curriculum learning–guided data mixing strategy, and a Korean-optimized tokenizer, Mi:dm 2.0 achieves deep contextual understanding of local nuances. Released under the MIT License in both general and lightweight variants, the model attains state-of-the-art zero-shot performance on Korean benchmarks such as KMMLU, significantly outperforming existing models and advancing the development of the K-intelligence ecosystem.

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Guide-to-Explain for Controllable Summarization

Nov 19, 2024arXiv.org

Large language models (LLMs) exhibit limited precision in controlling numerical attributes—such as summary length and extractiveness—in controllable summarization, hindering practical deployment aligned with user preferences. To address this, we propose a Guided Reflection Framework featuring a novel two-stage self-reflective mechanism: “Guide–Explain.” First, attribute-aware bias detection identifies deviations between the initial summary and target constraints; second, an attributional error explanation is generated to guide conditional regeneration. Our approach integrates self-reflective prompt engineering with multi-attribute joint constraints, significantly improving control fidelity and optimization efficiency. Experiments on multidimensional controllable summarization demonstrate substantial gains: constraint satisfaction rates increase markedly, and average iteration counts decrease by over 40% compared to state-of-the-art pure-LLM iterative baselines.

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Recent publications

Latest Papers

NOLLI: A Difficulty-Calibrated Puzzle Benchmark for Diagnosing the English-Korean Performance Gap

Aug 04, 2026

This study investigates the root causes of performance disparities between English and Korean tasks in large language models, focusing on the effects of writing systems, cultural context, and task complexity. To this end, we introduce NOLLI, a procedurally generated bilingual puzzle benchmark comprising 15 puzzle types (25 tasks, 7,500 instances), designed with behaviorally calibrated difficulty to ensure unique solutions and reproducibility. The benchmark features a three-tiered task structure: translation-matched pairs, Jamo-based adaptations, and culturally grounded Korean-only tasks. Innovatively replacing structural scale with behavioral calibration to define difficulty, our work introduces the first tasks explicitly leveraging Hangul’s Jamo components and Korean cultural knowledge. Experiments across 12 capable models reveal negligible performance gaps (±10%) on translation-matched tasks, but a pronounced 68.7% gap on tasks involving the Korean writing system. Notably, performance on Jamo composition tasks strongly predicts success on cryptographic reasoning, highlighting sub-syllabic multi-step inference as a critical bottleneck.

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ST-Veto: Spatio-Temporal Token Veto for Diffusion MLLMs via Taylor Prediction and Visual Grounding

Jul 20, 2026

Existing diffusion-based multimodal large language models (dMLLMs) suffer from the absence of effective confidence estimation and self-correction mechanisms during inference, limiting their generation quality. This work proposes ST-Veto, a training-free decoding strategy that, for the first time, integrates second-order Taylor expansion to predict confidence trends with visual attention quality assessment. Leveraging the order-agnostic generation property of diffusion models, ST-Veto dynamically identifies and replaces unstable or weakly grounded tokens at each decoding step. The approach enables joint spatiotemporal token-level optimization, achieving up to a 9% absolute accuracy gain across multiple dMLLMs and multimodal reasoning benchmarks. It significantly outperforms existing decoding and vision-language reasoning methods while introducing no additional training or computational overhead.

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