Papers by Jisu Lee
FineCite: A Novel Approach For Fine-Grained Citation Context Analysis (2025.findings-acl)
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| Challenge: | citation context analysis (CCA) studies the role and purpose of citations in scientific discourse. |
| Approach: | They construct a first comprehensive context definition based on semantic properties of citing text . they use fine-grained semantic properties to evaluate the definition . |
| Outcome: | The proposed definition shows improvements of up to 25% over state-of-the-art methods. |
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation (2025.findings-acl)
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| Challenge: | incorporating clinical symptom information into a model enhances domain expertise, improving its detection and interpretation performance. large language models are effective for generating explanatory rationales, but inconsistencies in relevance and domain alignment of LLM-generated rationale are challenging. |
| Approach: | They propose a framework that fine-tunes smaller language models with rationales exhibiting high domain relevance . they propose 'quality-focused' approach that selects rationale based on their alignment with clinical reasoning . |
| Outcome: | The proposed framework improves mental health detection and interpretation performance by ensuring high-quality rationales with domain relevance. |
Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation (2026.acl-long)
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| Challenge: | Existing static benchmarks for harmful content detection face limitations in scalability and diversity. |
| Approach: | They propose a framework for synthesizing harmful content using persona-guided large language model agents. |
| Outcome: | The proposed framework achieves a high success rate in harmful generation tests across multiple detection systems. |
ELF22: A Context-based Counter Trolling Dataset to Combat Internet Trolls (2022.lrec-1)
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| Challenge: | a new dataset aims to automate the method to counter trolls . trolleds cause psychological damage to individuals and increase social costs . |
| Approach: | They propose to use a dataset to generate counter responses by varying counter responses according to a given strategy. |
| Outcome: | The proposed method improves strategy-controlled sentence generation. |
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. |
Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling (2024.findings-emnlp)
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| Challenge: | Existing methods to counter trolling in online communities are not yet available to address the diversity of trolling behaviors. |
| Approach: | They propose a method for generating counter-responses to trolls by aligning these strategies with human preferences across different trolled contexts. |
| Outcome: | The proposed approach reduces negative effects of trolling and improves the online community environment. |
Ask LLMs Directly, “What shapes your bias?”: Measuring Social Bias in Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods to evaluate social bias in large language models have limitations . et al., 1995: stereotypes shape social perceptions without objective basis . |
| Approach: | They propose a method to intuitively quantify social perceptions and suggest metrics to evaluate biases within LLMs. |
| Outcome: | The proposed metrics capture the multi-dimensional aspects of social bias, the paper shows . they show that the proposed metrics can be used to evaluate bias in large language models . |
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. |
A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires (2023.acl-long)
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| Challenge: | Existing approaches to detecting mental disorders lack domain-based interpretation . lack of quality data or complexity of models can cause problems . |
| Approach: | They propose a model that captures semantic meanings directly from social media and compares them to symptom-related descriptions. |
| Outcome: | The proposed model outperforms baselines on mental disorder detection tasks. |
Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives (2026.acl-long)
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| Challenge: | Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates diverse peer perspectives to make a final decision. |
| Approach: | They define four key phenomena—social conformity, perceived expertise, dominant speaker effect, and rhetorical persuasion—and manipulate the number of adversaries, relative intelligence, argument length, and argumentative styles. |
| Outcome: | The results show that the reliability of the representative agent is undermined by the social context of its network. |