Papers by Jimin Lee
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval (2025.findings-naacl)
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| Challenge: | Existing methods to optimize retrieve-and-generate processes for real-world scenarios may not be optimal for large language models. |
| Approach: | They propose a Probing-RAG which utilizes hidden state representations from the intermediate layers of language models to adaptively determine the necessity of additional retrievals for a given query. |
| Outcome: | The proposed method outperforms previous methods while reducing the number of redundant retrieval steps. |
MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)
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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen
| Challenge: | Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed . |
| Approach: | They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities. |
| Outcome: | The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech . |
Think Clearly: Improving Reasoning via Redundant Token Pruning (2025.findings-emnlp)
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Daewon Choi, Jimin Lee, Jihoon Tack, Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi, Bhavana Ganesh, Jinwoo Shin, Aram Galstyan, Sravan Babu Bodapati
| Challenge: | Recent large language models show promising capabilities in long-form reasoning . however, they tend to include substantial redundancy in reasoning paths . |
| Approach: | They propose a structure-aware pruning method that prioritizes removing redundant tokens . they remove redundant token and then resume the reasoning generation . |
| Outcome: | The proposed method shows strong performance on reasoning-intensive benchmarks without training. |
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL (2025.emnlp-main)
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| Challenge: | Text-to-SQL aims to convert natural language questions into executable SQL queries. |
| Approach: | They propose a framework that generates and filters self-augmented examples for SQL generation . using self-generated examples, they surpass previous zero-shot and few-shot frameworks . |
| Outcome: | The proposed framework surpasses the previous zero-shot and few-shot frameworks, achieving higher execution accuracy. |
Towards Robust Mathematical Reasoning (2025.emnlp-main)
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Thang Luong, Dawsen Hwang, Hoang H Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu Hoang Trinh, Quoc V Le, Junehyuk Jung
| Challenge: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the international mathematical Olympiad level. |
| Approach: | They propose IMO-Bench, a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
| Outcome: | IMO-Bench is a suite of advanced reasoning benchmarks that targets the level of the international mathematical Olympiad. |
Accelerating Multilingual Language Model for Excessively Tokenized Languages (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have shown a significant degree of multilingual proficiency on a variety of tasks in multiple languages. |
| Approach: | They propose a framework to fine-tune a language model head and fine-track it while preserving its performance. |
| Outcome: | The proposed framework increases the generation speed by 1.7 while maintaining the performance of pre-trained multilingual models on target monolingual tasks. |
Code Defect Detection Using Pre-trained Language Models with Encoder-Decoder via Line-Level Defect Localization (2024.lrec-main)
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| Challenge: | Recent code Pre-trained Language Models (PLMs) have shown great success in code defect detection tasks. |
| Approach: | They propose a method that integrates line-level defect localization into a unified training process to identify which lines contain defects. |
| Outcome: | The proposed method significantly improves performance on four benchmark datasets for code defect detection. |
Fˆ2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax (2020.emnlp-main)
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| Challenge: | Existing methods for text generation do not fully reflect the rich diversity of human language. |
| Approach: | They propose to use F2-Softmax and MefMax to train a balanced frequency distribution using a frequency class-based method. |
| Outcome: | The proposed methods improve the diversity and quality of generated texts. |