Papers by Jisu Kim
Memorization or Reasoning? Exploring the Idiom Understanding of LLMs (2025.emnlp-main)
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| Challenge: | idioms have long posed a challenge due to their unique linguistic properties, which set them apart from other common expressions. |
| Approach: | They propose to use a large-scale dataset of idioms in six languages to evaluate LLMs' idiomatic processing ability. |
| Outcome: | The proposed model integrates contextual cues and reasoning to improve idiom understanding in LLMs, suggesting that their performance is influenced by memorization and reasoning. |
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021.emnlp-main)
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Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Jeon Dong Hyeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, Nako Sung
| Challenge: | GPT-3 has been used to train large-scale language models on hundreds of billion scale data. |
| Approach: | They propose a Korean variant of GPT-3 that uses Korean tokens to train in-context models. |
| Outcome: | The proposed method shows state-of-the-art zero-shot and few-shot learning on downstream tasks in Korean. |
Spotting Out-of-Character Behavior: Atomic-Level Evaluation of Persona Fidelity in Open-Ended Generation (2025.findings-acl)
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| Challenge: | Existing evaluation methods struggle to capture subtle inconsistencies in large language models. |
| Approach: | They propose an atomic-level evaluation framework that quantifies persona fidelity at a finer granularity. |
| Outcome: | The proposed framework detects inconsistencies that prior evaluation methods overlook . it captures subtle deviations that real users would encounter . |
RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs’ Contextual Sensitivity (2026.findings-acl)
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| Challenge: | a new benchmark measures the contextual sensitivity of large language models in role conflict scenarios . role conflicts are social dilemmas where multiple roles cannot be fulfilled simultaneously . authors: models are forced to arbitrate between dynamic contextual cues and learned preferences . |
| Approach: | They propose a benchmark to measure the contextual sensitivity of large language models in role conflict scenarios. |
| Outcome: | The proposed benchmark measures the contextual sensitivity of large language models in role conflict scenarios. |
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning (2023.acl-long)
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Kyuyong Shin, Hanock Kwak, Wonjae Kim, Jisu Jeong, Seungjae Jung, Kyungmin Kim, Jung-Woo Ha, Sang-Woo Lee
| Challenge: | Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. |
| Approach: | They propose to use user behavior sequences as plain text to represent rich information in any domain or system without losing generality. |
| Outcome: | The proposed frameworks achieve excellent results on diverse recommendation tasks and can be used on unseen domains and services. |