Papers by Namgi Han
A Multi-Perspective Analysis of Memorization in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can generate the same sequences contained in the pre-train corpus, known as memorization. |
| Approach: | They analyze the relationship between memorization and outputs from Large Language Models (LLMs) they show a sudden drop and increase in the frequency of input tokens when generating memorized/unmemorized sequences . |
| Outcome: | The proposed model can generate the same sequences contained in the pre-train corpus, and it can predict unmemorized tokens. |
Analyzing Word Embedding Through Structural Equation Modeling (2020.lrec-1)
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| Challenge: | Existing studies have shown that word embedding improves accuracy on NLP tasks. |
| Approach: | They propose a causal diagram based on the evaluation results of word embeddings using partial least squares path modeling. |
| Outcome: | The proposed model proves that word embedding contributes to solving downstream tasks. |
An empirical analysis of existing systems and datasets toward general simple question answering (2020.coling-main)
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| Challenge: | evaluators of simple factoid question answering using different datasets are not able to solve SimpleQuestions. |
| Approach: | They evaluate the progress of the field toward solving simple factoid questions over a knowledge base. |
| Outcome: | The proposed model is nearly solved on the most popular dataset, but not on the robustness of existing systems. |
Sound of Story: Multi-modal Storytelling with Audio (2023.findings-emnlp)
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| Challenge: | Existing studies on storytelling with sound have focused on visuals and sounds, but little attention has been given to sound. |
| Approach: | They propose to establish a new component called background sound which is story context-based audio without any linguistic information. |
| Outcome: | The proposed dataset is the largest well-curated dataset for storytelling with sound . it contains 27,354 stories with 19.6 images per story and 984 hours of speech-decoupled audio . |
A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Models (2025.acl-long)
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| Challenge: | Membership Inference Attack (MIA) is a method that differentiates trained (member) and untrained (non-member) data. |
| Approach: | They used thousands of experiments to examine membership inference attacks from different settings and then revisited them with thousands of different methods. |
| Outcome: | The proposed methods outperform baselines in the study and improve with model size and varies with domains. |