Papers by Jiyeon Kim
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding (2021.findings-emnlp)
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| Challenge: | Existing methods for learning semantic similarity between two English sentences have focused on one sub-task and therefore showed biased performance. |
| Approach: | They propose a method to learn semantic similarity between two English sentences using siamese networks. |
| Outcome: | The proposed method improves on both sub-tasks and predicts similarity scores in 14 languages. |
Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams (2026.acl-long)
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Jiyeon Kim, Hyunji Lee, Dylan Zhou, Sue Hyun Park, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Sungmin Cha, Minjoon Seo
| Challenge: | Existing models and agentic memory systems fail to adapt robustly to OAKS, demonstrating delays in state-tracking and susceptibility to distraction within streaming environments. |
| Approach: | They propose a benchmark to evaluate models' ability to adapt to changing knowledge over streaming . they use two datasets to analyze how facts evolve over time . |
| Outcome: | The proposed benchmark evaluates models in an online adaptation setting over streaming, continually updating knowledge. |
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding (2024.naacl-long)
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| Challenge: | End-to-end learning models require large volume of speech data with intent labels . however, models are sensitive to inconsistencies between training and evaluation conditions . |
| Approach: | They propose a module-based approach to learn intent in a noisy-channel model . they correlate error patterns between clean and noisy ASR transcripts . |
| Outcome: | The proposed method outperforms existing methods and improves in noisy environments. |
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval (2024.acl-long)
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| Challenge: | Existing listwise reranking models rely on pointwise sizing of each passage . Until now, listwise models lack the ability to compare between passages at inference time . |
| Approach: | They propose a listwise reranking approach based on Fusion-in-Decoder that handles multiple candidate passages at train and inference time. |
| Outcome: | The proposed model outperforms the state-of-the-art RankT5 model on the BEIR benchmark for zero-shot retrieval task with a notable +1.3 gain in the average NDCG@10 score. |