Papers by HyeongSik Kim
Over-Generation and Compaction: A Prompting Strategy for Procedural Text Adaptation with Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing prompting strategies for large language models often yield superficial or erroneous adaptations due to alignmentinduced biases and the inherent complexity of procedural editing. |
| Approach: | They propose an overgenerationandcompaction prompting strategy that leverages the model’s latent knowledge and compacts them into concise, coherent adaptations. |
| Outcome: | The proposed approach improves adaptation consistency and feasibility compared to baseline prompting methods without additional fine-tuning or curated training resources. |
A Textual Dataset for Situated Proactive Response Selection (2023.acl-long)
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| Challenge: | Recent data-driven conversational models can return fluent, consistent, and informative responses to many kinds of requests and utterances in task-oriented scenarios. |
| Approach: | They propose a task of proactive response selection based on situational information and a dataset of 1.7k English conversation examples that include situational background information and for each conversation a set of responses. |
| Outcome: | The proposed model can only provide fluent, consistent, and informative responses to a set of 1.7k English conversation examples and is not easy to perform for strong neural models. |