Papers by Hongjin Kim
Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation (2025.coling-main)
Copied to clipboard
| Challenge: | KEEM is a dynamically generated dataset designed to enhance memory updates in long-term conversational systems. |
| Approach: | They propose a dataset that keeps emotional and essential memories and generates integrative memories that incorporate emotional context and causal relationships. |
| Outcome: | The Keep Emotional and Essential Memory (KEEM) dataset enhances memory updates in long-term conversational systems. |
Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection (2026.acl-long)
Copied to clipboard
| Challenge: | Cognitive distortions are systematic errors in thinking that occur when individuals perceive and interpret external information, leading to a negative conclusion that does not correspond to reality. |
| Approach: | They propose a framework that combines Large Language Models with a Multiple-Instance Learning architecture to enhance interpretability and expression-level reasoning. |
| Outcome: | The proposed framework improves interpretability and expression-level reasoning on Korean and English datasets. |
Exploring the Impact of Instruction-Tuning on LLM’s Susceptibility to Misinformation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies highlight that large language models are receptive to external information that contradicts their parametric knowledge, but little research has been conducted on the direct impact of instruction-tuning on this phenomenon. |
| Approach: | They examine how instruction-tuning influences LLMs' susceptibility to misinformation, particularly in knowledge conflict situations. |
| Outcome: | The proposed model is more user-oriented and more likely to accept misinformation when it is presented by the user. |
Title-based Extractive Summarization via MRC Framework (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing studies on extractive summarization focus on scoring and selecting summary sentences . existing models tend to select generalized sentences while overlooking the overall content of a document. |
| Approach: | They propose a machine reading comprehension framework for extractive summarization by setting a query as the title. |
| Outcome: | The proposed framework outperforms existing models on long and short summaries in Korean and English . it can consider the semantic coherence and relevance of summary sentences in relation to the overall content . |
Analyzing Key Factors Influencing Emotion Prediction Performance of VLLMs in Conversational Contexts (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that large language models and vision large language model (VLLMs) possess EI and the ability to understand emotional stimuli in the form of text and images. |
| Approach: | They analyze the key elements affecting the emotion prediction performance of VLLMs in conversational contexts. |
| Outcome: | The proposed model performance was compared with other models in a conversational context. |
Exploring Nested Named Entity Recognition with Large Language Models: Methods, Challenges, and Insights (2024.emnlp-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a challenging task in natural language processing . nested NER requires sophisticated techniques to identify entities within entities . |
| Approach: | They investigate the application of Large Language Models (LLMs) to nested NER . they find methodologies from previous work are less effective . |
| Outcome: | The proposed methods outperform BERT-based models in nested NER tasks . however, they do not outperformed the existing models on the GENIA dataset . |
A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for building multimodal open-domain dialogue agents based on large datasets are limited in real-world settings . |
| Approach: | They propose a new learning strategy called vision-language warm-up tasks for multimodal dialogue models that relies solely on learning from target data. |
| Outcome: | The proposed learning strategy achieves comparable and in some cases superior performance compared to existing state-of-the-art models on various evaluation metrics. |
Can Large Language Models Differentiate Harmful from Argumentative Essays? Steps Toward Ethical Essay Scoring (2025.coling-main)
Copied to clipboard
| Challenge: | Existing automated essay scoring systems overlook ethical and moral aspects of content, erroneously assigning high scores to essays that propagate harmful opinions. |
| Approach: | They introduce a Harmful Essay Detection benchmark to test the effectiveness of various Large Language Models (LLMs) they find that current AES systems overlook ethically and morally problematic elements in essays . |
| Outcome: | The proposed benchmark compared LLMs and AES models to identify and score harmful essays. |