Papers by Seoyeon Kim
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
Copied to clipboard
| Challenge: | Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality. |
| Approach: | They propose a framework that leverages commonsense-based persona expansion to address such issues in long-term conversations. |
| Outcome: | The proposed framework facilitates better response generation via human-like persona refinement. |
Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. |
| Approach: | They propose to use a synthetic English-korean CS question-answering dataset to investigate this potential. |
| Outcome: | The proposed model can activate, identify and leverage knowledge for reasoning in low-resource languages. |
Extending CLIP’s Image-Text Alignment to Referring Image Segmentation (2024.naacl-long)
Copied to clipboard
| Challenge: | Referring Image Segmentation (RIS) is a cross-modal task that aims to segment an instance described by a natural language expression. |
| Approach: | They propose a framework that leverages the cross-modal nature of CLIP for RIS by leveraging image-text alignment knowledge in CLIP's image-embedding space. |
| Outcome: | The proposed framework outperforms CLIP-based methods on all three major RIS benchmarks and outperformed previous CLIP methods. |
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Recent approaches in domain-specific named entity recognition (NER) have shown remarkable advances, but they still lack faithfulness, producing erroneous predictions. |
| Approach: | They propose a framework that revises errors from existing NER methods using knowledge to produce more faithful predictions. |
| Outcome: | The proposed framework can validate errors from existing models as a model-agnostic approach. |