Papers by Eunho Yang
Bringing Real-World Relations into Video Generation with Graph-Structured Knowledge (2026.acl-long)
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| Challenge: | Existing text-to-video models struggle to accurately simulate real-world physics and dynamic entity interactions. |
| Approach: | They propose a framework that integrates graph-structured temporal knowledge into video latent diffusion models to enhance compositional generation and interaction fidelity. |
| Outcome: | The proposed framework enhances compositional generation and interaction fidelity by integrating graph-structured temporal knowledge into video latent diffusion models. |
Does it Really Generalize Well on Unseen Data? Systematic Evaluation of Relational Triple Extraction Methods (2022.naacl-main)
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| Challenge: | Existing extraction models memorize and recall already seen triples but cannot generalize effectively for unseen triples. |
| Approach: | They propose a method to generalize existing extraction models by rearranging datasets and augmenting test sets. |
| Outcome: | The proposed method can significantly increase the generalization performance of existing models. |
PhaseMI: A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM-based Counseling (2026.findings-acl)
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| Challenge: | Existing MI datasets do not explicitly model structured progression of MI phases, which is essential for effective and goal-oriented counseling. |
| Approach: | They propose a phase-structured MI dataset with a data generation framework that employs therapist, client, and supervisor LLMs to explicitly control phase transitions. |
| Outcome: | The proposed model achieves 12.3% better coverage of MI phases, 37.6% in guiding, and 61.1% in choosing. |
Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens (2025.emnlp-main)
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| Challenge: | Large Vision-Language Models (LVLMs) generate contextually relevant responses by jointly interpreting visual and textual inputs. |
| Approach: | They propose a method to classify whether an input token is visually grounded by reinterpreting question prompts or replacing the detected absent tokens during generation. |
| Outcome: | The proposed method mitigates the models’ tendency to falsely presume the visual presence of text input and its generality across various LVLMs. |
Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation (2025.emnlp-industry)
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| Challenge: | Recent advances in Large Language Models have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. |
| Approach: | They propose a method that rebalances the turn-count distribution of training data to mitigate Format Inertia in medical pre-consultation tasks. |
| Outcome: | The proposed method significantly alleviates Format Inertia in medical pre-consultation tasks. |
Distilling Linguistic Context for Language Model Compression (2021.emnlp-main)
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| Challenge: | Knowledge distillation is a major technique for deploying vast language models in resource-strapped environments. |
| Approach: | They propose a method that transfers contextual knowledge via Word Relation and Layer Transforming Relation. |
| Outcome: | The proposed method is able to transfer contextual knowledge without restrictions on architectural changes between teacher and student on language understanding tasks. |
Taxonomy of Comprehensive Safety for Clinical Agents (2025.emnlp-industry)
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| Challenge: | Existing methods for ensuring safety in clinical chatbot applications are not suitable for clinical applications. |
| Approach: | They propose a fine-grained taxonomy that integrates safety filtering and tool selection into a single user intent classification step. |
| Outcome: | The proposed taxonomy integrates safety filtering and tool selection into a single user intent classification step. |
Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines (2026.eacl-industry)
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Jean Seo, Gibaeg Kim, Kihun Shin, Seungseop Lim, Hyunkyung Lee, Wooseok Han, Jongwon Lee, Eunho Yang
| Challenge: | EPAG is a benchmark dataset and evaluation pipeline for pre-consultation of large language models. |
| Approach: | They propose a benchmark dataset and framework for evaluating pre-consultation ability of LLMs using diagnostic guidelines. |
| Outcome: | The proposed framework outperforms frontier LLMs in pre-consultation. |
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices (2025.naacl-long)
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| Challenge: | Existing methods for quantizing weights and activations of large language models suffer from non-negligible accuracy drops, especially on massive multitask language understanding. |
| Approach: | They propose a weight-activation quantization method that reconstructs the outputs of an intermediate Transformer block by leveraging low-rank weight-scaling matrices. |
| Outcome: | The proposed method reduces the complexity of the weight-activation quantization techniques while achieving high throughput and reducing inference costs. |
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning (2024.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. |
| Approach: | They propose a method that utilizes prompt tuning to enable generative language models to transfer student-friendly knowledge. |
| Outcome: | Extensive experiments on instruction-following datasets show that PromptKD achieves state-of-the-art performance while adding only 0.0007% of the teacher’s parameters as prompts. |
CURaTE: Continual Unlearning in Real Time with Ensured Preservation of LLM Knowledge (2026.findings-acl)
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| Challenge: | Existing methods for unlearning specific pieces of knowledge are ineffective due to the inability to filter out in advance all potentially problematic data. |
| Approach: | They propose a method for unlearning specific pieces of knowledge after training . they use a sentence embedding model to create sharp decision boundaries . |
| Outcome: | The proposed method achieves more effective forgetting than existing methods and maintains near perfect knowledge preservation over any number of updates. |