Papers by YoungBin Kim
CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples (2025.emnlp-main)
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| Challenge: | Spurious correlations are patterns that appear in datasets but do not represent genuine relationships. |
| Approach: | They propose a more general form of counterfactual data augmentation that tackles multiple biases . they propose 'CoBA' that decomposes text into subject-predicate-object triples and modifies them to disrupt spurious correlations. |
| Outcome: | The proposed framework reduces biases and strengthens out-of-distribution resilience. |
From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse (2025.findings-emnlp)
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| Challenge: | a recent study using LLMs has relied on outdated datasets and limited generalization ability on unseen texts. |
| Approach: | They construct a large-scale dataset of political discourse and use it to make three judgments . they identify distinct patterns and demonstrate tendencies of label agreement using a leave-one-out strategy. |
| Outcome: | The proposed approach is applicable in real-world settings with inherent constraints. |
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset Generation (2024.emnlp-main)
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| Challenge: | Pre-trained language models have limited applicability for inference due to their large parameter size and limited generalization ability. |
| Approach: | They propose a method that generates a dataset regardless of the target domain . this allows for generalization of the tiny task model to any domain that shares the label space . |
| Outcome: | The proposed method achieves generalizability across domains while using a parameter set that is orders of magnitude smaller than PLMs. |
IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method (2024.emnlp-main)
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| Challenge: | Pre-trained Language Models (PLMs) have shown remarkable performance on diverse NLP tasks through pre-training and fine-tuning. |
| Approach: | They propose a numerically robust IM-connection incorporating a layer of BERT as a solution of Ordinary Differential Equations (ODEs) . Experimental results validate the robustness of IM BERT under various conditions. |
| Outcome: | The proposed model outperforms the existing model on the adversarial GLUE dataset by 5.9%p on low-resource scenarios. |
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs (2026.findings-acl)
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| Challenge: | Existing preference learning-based approaches rely on proprietary models to construct preference datasets, causing a distributional mismatch between the proprietary and target models. |
| Approach: | They propose a framework that aligns LVLMs using in-distribution data derived from the model's intrinsic knowledge. |
| Outcome: | The proposed framework surpasses baselines in hallucination mitigation while requiring only 5.2k samples. |
LLM Agents at the Roundtable: A Multi-Perspective and Dialectical Reasoning Framework for Essay Scoring (2025.findings-emnlp)
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| Challenge: | a new framework for automated essay scoring is needed to achieve multi-perspective understanding and judgment. |
| Approach: | They propose a roundtable essay scoring framework that performs precise and human-aligned scoring under a zero-shot setting. |
| Outcome: | The proposed framework outperforms previous zero-shot AES approaches by enabling collaboration among agents with diverse evaluation perspectives. |
Position Paper: How Should We Responsibly Adopt LLMs in the Peer Review Process? (2026.findings-eacl)
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| Challenge: | a recent paper criticizes the current use of Large Language Models (LLMs) for simple review text generation. |
| Approach: | They propose to use Large Language Models to support key aspects of the review process . they argue that this approach overlooks more meaningful applications of LLMs . authors argue that the increased reviewing burden per reviewer is a factor . |
| Outcome: | The proposed approach would support reproducibility, correctness and relevance of citations and ethics review flagging. |
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation (2024.emnlp-main)
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| Challenge: | Various attempts to correct noisy data in the construction process have been made, but human annotation is expensive and time-consuming. |
| Approach: | They propose to use large language models for data annotation to imitate human annotation and classify unrelated documents from a multi-document summarization task. |
| Outcome: | The proposed method imitates human annotation and classifies unrelated documents from the Multi-News dataset. |
FairQE: Multi-Agent Framework for Mitigating Gender Bias in Translation Quality Estimation (2026.acl-long)
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| Challenge: | Existing QE models exhibit systematic gender bias, especially in gender-ambiguous contexts. |
| Approach: | They propose a multi-agent-based, fairness-aware QE framework that mitigates gender bias in both gender-ambiguous and gender-explicit scenarios. |
| Outcome: | The proposed framework mitigates gender bias in gender-ambiguous and gender-explicit scenarios while maintaining the strengths of existing models. |
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks (2026.findings-acl)
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| Challenge: | Large Vision-Language Models (LVLMs) are capable of learning from vast webscale datasets but pose privacy risks as they can unintentionally memorize sensitive information. |
| Approach: | They propose a Reliable Multi-hop and Multi-image Memorization Benchmark that ensures robust foundational learning through principled data scaling and reasoning-aware QA pairs. |
| Outcome: | Extensive experiments show that ReMem provides a reliable framework for diagnosing both learning and unlearning behaviors in Large Vision-Language Models. |
Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks (2024.findings-naacl)
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| Challenge: | Offensive language detection is important for filtering out abusive expressions, authors argue . authors propose user-intended adversarial attacks that insert special symbols or leverage distinctive features of the Korean language. |
| Approach: | They propose user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language. |
| Outcome: | The proposed models are more robust to performance degradation even when the attack rate is increased, compared to models trained on noisy texts. |
Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) are renowned for their extensive linguistic knowledge and strong generalization capabilities, but their high computational demands make them unsuitable for resource-constrained environments. |
| Approach: | They propose a framework that integrates a single frozen layer from an LLM into a SLM and fine-tunes the combined model for specific tasks. |
| Outcome: | The proposed framework improves performance across a range of natural language processing tasks, including both natural language understanding and generation. |
Delving into Multilingual Ethical Bias: The MSQAD with Statistical Hypothesis Tests for Large Language Models (2025.acl-long)
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| Challenge: | Despite recent strides in large language models, studies have highlighted the existence of social biases within them . a recent study examined the ethical biase of LLMs concerning globally discussed topics . |
| Approach: | They propose to validate and compare ethical biases of large language models . they use news articles and socially sensitive questions to generate a data set . |
| Outcome: | The proposed dataset shows that ethical biases are widespread across languages and topics . the null hypothesis was rejected in most cases, suggesting biase arising from language differences. |
IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering (2026.acl-long)
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| Challenge: | Existing prompt compression methods are designed for single-turn queries and fail to capture interdependent reasoning steps. |
| Approach: | They propose a unified, training-free prompt compression framework that integrates multi-hop reasoning within an iterative compression loop. |
| Outcome: | Experiments on MusiQue, 2WikiMultiHopQA, and HotpotQA show that iterCOMP achieves significant improvements in Exact Match and F1 scores while reducing the token budget. |
Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models (2025.findings-emnlp)
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| Challenge: | Large language models have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. |
| Approach: | They extend existing DST dataset by generating utterances of a second user based on speech act theory. |
| Outcome: | The proposed model incorporates utterances of a second user into conversations, enabling a controlled evaluation of LLMs in multi-user settings. |
See-Saw Modality Balance: See Gradient, and Sew Impaired Vision-Language Balance to Mitigate Dominant Modality Bias (2025.naacl-long)
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| Challenge: | Vision-language models often rely on a single modality rather than treating and utilizing them equally, leading to dominance of a specific modality on the overall performance. |
| Approach: | They propose a framework to mitigate dominant modality bias by adjusting the gradient of KL divergence based on each modality's contribution and aligning task directions in a non-conflicting manner. |
| Outcome: | The proposed framework mitigates dominant modality bias on UPMC Food-101, Hateful Memes, and MM-IMDb datasets. |
Making Sense of Korean Sentences: A Comprehensive Evaluation of LLMs through KoSEnd Dataset (2025.acl-srw)
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| Challenge: | Despite advances in LLMs, there are still concerns about their effectiveness with low-resource agglutinative languages compared to English. |
| Approach: | They evaluated 11 LLMs to assess their understanding of Korean sentence endings . they found that explicitly considering linguistic features improved performance . |
| Outcome: | The evaluated LLMs were able to understand Korean sentences better than other languages. |
GPTs Are Multilingual Annotators for Sequence Generation Tasks (2024.findings-eacl)
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| Challenge: | Existing methods of data annotation are time-consuming and expensive . complexity of crowdsourcing increases when dealing with low-resource languages . |
| Approach: | They propose an autonomous method to gather unlabeled data and label them using large language models. |
| Outcome: | The proposed method is cost-efficient and applicable for low-resource language annotation. |
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes (2024.eacl-srw)
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| Challenge: | Existing methods for text data augmentation suffer from potential semantic damage due to the discrete nature of sentences. |
| Approach: | They propose to adapt AutoAugment to solve this problem by using softEDA to increase text data. |
| Outcome: | The proposed method can boost existing augmentation methods and enhance cutting-edge pretrained language models. |