Papers by Ji Won Yoon
SelFusion: Self-distillation for Diffusion Language Models (2026.acl-long)
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| Challenge: | Existing knowledge distillation methods for autoregressive large language models (LLMs) are not effective for reducing generation quality, but they can be useful for real-time applications. |
| Approach: | They propose a self-distillation framework that allows for effective KD without external teacher . they propose to use two modes of knowledge distillation to determine distillation direction . |
| Outcome: | The proposed framework outperforms existing methods with external teachers on instruction-following tasks. |
CCQA: Generating Question from Solution Can Improve Inference-Time Reasoning in SLMs (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have yielded remarkable performance across a wide range of tasks, including machine translation, code generation, sentiment analysis, and reasoning. |
| Approach: | They propose a new reasoning method that generates a question from each reasoning path and answer, evaluates each by its similarity to the original question, and selects the candidate solution with the highest similarity score as the final answer. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on mathematical and commonsense reasoning benchmarks and establishes a new practical baseline for efficient reasoning in SLMs. |