Papers by Minseok Choi
MemeInterpret: Towards an All-in-One Dataset for Meme Understanding (2025.findings-emnlp)
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Jeongsik Park, Khoi P. N. Nguyen, Jihyung Park, Minseok Kim, Jaeheon Lee, Jae Won Choi, Kalyani Ganta, Phalgun Ashrit Kasu, Rohan Sarakinti, Sanjana Vipperla, Sai Sathanapalli, Nishan Vaghani, Vincent Ng
| Challenge: | Existing research has not explored meme captioning's decomposition into subtasks or its connections to other CMU tasks. |
| Approach: | a new meme corpus is built upon the Facebook Hateful Memes dataset . it contains meme captions, corresponding surface messages and relevant background knowledge . |
| Outcome: | a new corpus of meme captions and surface messages unifies three major categories of CMU tasks for the first time. |
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models (2024.findings-acl)
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| Challenge: | Language models (LMs) demonstrate exceptional capabilities on tasks, but are vulnerable to extraction attacks. |
| Approach: | They propose Privacy Protection via Optimal Parameters (POP) which induces the model to forget about some of its training data. |
| Outcome: | The proposed method outperforms the state-of-the-art in retaining LM performance on 9 classification and 4 dialogue benchmarks. |
HistRED: A Historical Document-Level Relation Extraction Dataset (2023.acl-long)
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| Challenge: | Relation extraction (RE) tasks are limited to sentencelevel RE, but are not feasible in real-world applications. |
| Approach: | They propose a bilingual relation extraction model that leverages both Korean and Hanja contexts to predict relations between entities. |
| Outcome: | The proposed model outperforms monolingual baselines on histRED . it supports various self-contained subtexts with different lengths . |
Word2Passage: Word-level Importance Re-weighting for Query Expansion (2025.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) enhances the quality of LLM generation by providing relevant chunks, but retrieving accurately from external knowledge remains challenging due to missing contextually important words in query expansion. |
| Approach: | They propose a method that generates word, sentence, and passage references for query expansion and assigns distinct importance scores to words based on their origin and characteristics. |
| Outcome: | The proposed method outperforms existing methods across datasets and LLM configurations, effectively enhancing retrieval accuracy and generation quality. |
SimCKP: Simple Contrastive Learning of Keyphrase Representations (2023.findings-emnlp)
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| Challenge: | Existing models for keyphrase generation and keyphrase extraction use a token level to generate keyphrases that do not appear in a document. |
| Approach: | They propose a simple contrastive learning framework that generates keyphrases that do not appear in a document and a reranker that adapts the scores for each generated phrase. |
| Outcome: | The proposed model outperforms the state-of-the-art models on multiple benchmark datasets. |
Rethinking Style Transformer with Energy-based Interpretation: Adversarial Unsupervised Style Transfer using a Pretrained Model (2022.emnlp-main)
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Hojun Cho, Dohee Kim, Seungwoo Ryu, ChaeHun Park, Hyungjong Noh, Jeong-in Hwang, Minseok Choi, Edward Choi, Jaegul Choo
| Challenge: | Existing methods to train text style transfer models with adversarial loss degrade fluency compared to other metrics. |
| Approach: | They propose a method which leverages a pretrained language model to improve fluency by restructuring the discriminator and the model itself. |
| Outcome: | The proposed model achieves state-of-the-art on three public benchmarks and achieved state-outperformance on the overall metrics. |
Cross-Lingual Unlearning of Selective Knowledge in Multilingual Language Models (2024.findings-emnlp)
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| Challenge: | Pretrained language models memorize large amounts of information, raising significant safety concerns. |
| Approach: | They propose an approach to machine unlearning for multilingual language models that selectively erases information across different languages while maintaining overall performance. |
| Outcome: | The proposed approach is compared with existing unlearning baselines and set a new standard for secure and adaptable multilingual language models. |
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport (2025.acl-long)
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| Challenge: | Instruction-following large language models (LLMs) inadvertently disclose private, sensitive information to their users, underscoring the need for machine unlearning techniques to remove selective information from the models. |
| Approach: | They propose an optimal transport-based unlearning method that utilizes the Wasserstein distance from the model’s initial parameters to achieve more effective and fine-grained unlearning. |
| Outcome: | The proposed method surpasses existing methods and establishes a new standard for secure and adaptable LLMs that can accommodate user data removal requests without the need for full retraining. |