Artificial Intelligence and Intelligent Systems

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  3. Artificial Intelligence and Intelligent Systems

Artificial Intelligence and Intelligent Systems Group

Professors: Eunok Paek, Sang-Wook Kim, Yong Suk Choi, Taeuk Kim, Eun-Sol Kim, Yung-Kyun Noh, Sungyoon Lee, Dong-Kyu Chae

Artificial intelligence aims to mimic human intelligence in order to help or replace humans at various tasks. We are conducting fundamental research on cognition, inference, and learning to overcome the limitations of current approaches and implement highly intelligent systems in real life. We are also developing practical technologies including visual perception and 3D reconstruction of environments for autonomous driving and robot navigation, machine intelligence based on ontology, intelligent information retrieval and collaborative recommendation, SNS text mining, smart learning, semantic web, biological data mining, etc.

Selected Publications

  • Jang, M.-H., J.-M. Park, Y. Ko, and Sang-Wook Kim. “Accelerating storage-based training for graph neural networks.” In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM. 2026.
  • Seo, D.-H., S. Kim, T. Kim, W.-Y. Shin, and Sang-Wook Kim. “SCOUT: Structure-aware aspect and anchor-count selection for positional attribute augmentation.” In Proceedings of the ACM Web Conference (WWW). ACM, 2026.
  • Kim, M.-J., J. Son, Y.-C. Lee, and Sang-Wook Kim. “Retracing and restoring: Chronological context preservation for effective dynamic recommendation.” In Proceedings of the ACM Web Conference (WWW). ACM. 2026.
  • Ryu, S., and Sang-Wook Kim. “PANther: Party-specific attention-based networks for political perspective detection.” In Proceedings of the ACM Symposium on Applied Computing (SAC). ACM. 2026.
  • Kim, T., J. Kim, W.-Y. Shin, and Sang-Wook Kim. “Leveraging retrieval-augmented language models for accurate item/feature selection in conversational recommender systems.” In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM). ACM. 2026.
  • Ko, Y., D. E. Lee, S. K. Yu, and Sang-Wook Kim. “Learning short-term and long-term patterns of high-order dynamics in real-world networks.” In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2025.
  • Ko, Y., S. Ryu, and Sang-Wook Kim. “Is this news still interesting to you?: Lifetime-aware interest matching for news recommendation.” In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2025.
  • Jang, M.-H., M.-K. Shin, T. Park, Y. Park, and Sang-Wook Kim. “An efficient PIM-based graph engine on a single machine.” In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 2025.
  • Park, J.-M., M.-H. Jang, and Sang-Wook Kim. “RealGraphGPUWeb: A convenient and efficient GPU-based graph analysis platform on the web.” Journal of the Korean Institute of Smart Media (KISM) (2025).
  • Kim, T., S. Ban, H. Kim, and Sang-Wook Kim. “STARLINE: Contrastive learning with modality-aware graph refinement for effective multimedia recommendation.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM, 2025.
  • Reyhani Hamedani, M., J.-S. Ryu, and Sang-Wook Kim. “SIGEM: A simple yet effective similarity based graph embedding method.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Vol. 2. ACM, 2025.
  • Son, J., J. Kim, T. Kim, Y.-C. Lee, and Sang-Wook Kim. “CATER: A cluster-based alternative-term recommendation framework for large-scale web search at NAVER.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (pp. 2548-2559). ACM, 2025.
  • Lee, Y.-C., J. Lee, M. Yamashita, D. Lee, and Sang-Wook Kim. “CAPER: Enhancing career trajectory prediction using temporal knowledge graph and ternary relationship.” In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM, 2025.
  • Bae, H.-K., J. Kim, J. Lee, and Sang-Wook Kim. “DUET: Dually guided knowledge distillation from explicit feedback.” Information Fusion 120 (2025): 103098.
  • Ko, Y., H. Tong, and Sang-Wook Kim. “Enhancing hyperedge prediction with context-aware self-supervised learning.” IEEE Transactions on Knowledge and Data Engineering 37, no. 4 (2025): 1772-1784.
  • Son, J., H. Kim, and Sang-Wook Kim. “Rating-aware homogeneous review graphs and user likes/dislikes differentiation for effective recommendations.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 2070-2080). ACM, 2025.
  • Jeong, D., T. Kim, D. Cho, and Sang-Wook Kim. “MELON: Learning multi-aspect modality preferences for accurate multimedia recommendation.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 1850-1859). ACM, 2025.
  • Xu, J., Z. Chen, W. Wang, X. Hu, Sang-Wook Kim, and E. C. H. Ngai. “COHESION: Composite graph convolutional network with dual-stage fusion for multimodal recommendation.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (pp. 1830-1839). ACM, 2025.
  • Jang, M.-H., I. Jo, D.-H. Bae, and Sang-Wook Kim. “RealGraphOF: A high-performance graph engine for very large graph analysis.” In Proceedings of the ACM Web Conference (WWW). ACM, 2025.
  • Bae, H.-K., H.-R. Jang, W.-Y. Shin, and Sang-Wook Kim. “Ranking items by the current-preferences and profits: A list-wise learning-to-rank approach to profit maximization.” In Proceedings of the ACM Web Conference (WWW) (pp. 5010-5021). ACM, 2025.
  • Yu, S. K., D. E. Lee, Y. Ko, and Sang-Wook Kim. “HyGEN: Regularizing negative hyperedge generation for accurate hyperedge prediction.” In Companion Proceedings of the ACM Web Conference (WWW) (pp. 1500-1504). ACM, 2025.
  • Seo, D.-H., H. Shin, and Sang-Wook Kim. “Exploring search volumes of terms in web portals for accurate event-aware traffic prediction.” In Companion Proceedings of the ACM Web Conference (WWW) (pp. 1293-1297). ACM, 2025.
  • Ko, Y., S. Ryu, and Sang-Wook Kim. “CROWN: A novel approach to comprehending users' preferences for accurate personalized news recommendation.” In Proceedings of the ACM Web Conference (WWW) (pp. 1911-1921). ACM, 2025.
  • Son, J., D.-H. Seo, J. Song, K. Han, N. Kim, and Sang-Wook Kim. “A novel traffic flow prediction model based on a direct spatio-temporal graph.” In Proceedings of the ACM Symposium on Applied Computing (SAC) (pp. 415-424). ACM, 2025.
  • Kim, T., J. Heo, H. Kim, and Sang-Wook Kim. “HI-DR: Exploiting health status-aware attention and an EHR Graph+ for effective medication recommendation.” Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11950-11958.
  • Kim, T., H. Jang, J. Yu, T. Kim, H. Lee, J.-H. Im, and Sang-Wook Kim. “ESPRESSO: An effective approach to passage retrieval for high-quality conversational recommender systems.” Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11942-11949.
  • Lee, Y.-C., H. Shin, and Sang-Wook Kim. “Disentangling, amplifying, and debiasing: Learning disentangled representations for fair graph neural networks.” Proceedings of the AAAI Conference on Artificial Intelligence 39 (2025): 12013-12021.
  • Kim, J., S. Lee, and Taeuk Kim. “Think just enough: Leveraging self-assessed confidence for adaptive reasoning in language models.” In Findings of the Association for Computational Linguistics: EACL 2026 (pp. 5000-5006). Association for Computational Linguistics, 2026.
  • Kim, Y., J. Kim, Taeuk Kim, and H.-C. Cho. “Administrative decision-making with generative AI: The challenge of epistemic boundedness.” Administration & Society 58, no. 2 (2026): 284-304.
  • Kim, J., Y. Shin, U. Hwang, J. Choi, R. Xuan, and Taeuk Kim. “Memorization or reasoning?.” Exploring the idiom understanding of LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 21678-21699). Association for Computational Linguistics, 2025.
  • Yoon, Y., Y. Son, N. So, M. Kim, M. Cho, C. Park, S. Lee, and Taeuk Kim. “Beyond task-oriented and chitchat dialogues: Proactive and transition-aware conversational agents.” In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 13291-13317). Association for Computational Linguistics, 2025.
  • Lee, J., K. Lee, and Taeuk Kim. “MAGIC: A multi-hop and graph-based benchmark for inter-context conflicts in retrieval-augmented generation.” In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 8783-8803). Association for Computational Linguistics, 2025.
  • Lee, H., U. Hwang, H. Lim, and Taeuk Kim. “Does localization inform unlearning?.” A rigorous examination of local parameter attribution for knowledge unlearning in language models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 21868-21880). Association for Computational Linguistics, 2025.
  • Kim, J., G. Lee, Taeuk Kim, and K. Shin. “KGMEL: Knowledge graph-enhanced multimodal entity linking.” In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, 2025.
  • Kim, H. J., Y. Kim, S. Lee, and Taeuk Kim. “When to speak, when to abstain: Contrastive decoding with abstention.” In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 9710-9730). Association for Computational Linguistics, 2025.
  • Lee, W., M. Kim, Taeuk Hur, G. H. Jang, W. Lee, M. Na, and Taeuk Kim. “ENGinius: A bilingual LLM optimized for plant construction engineering.” In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track). Association for Computational Linguistics, 2025.
  • Kim, S., C. Kim, and Taeuk Kim. “FCMR: Robust evaluation of financial cross-modal multi-hop reasoning.” In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 23352-23380). Association for Computational Linguistics, 2025.
  • Ko, Y., H. Yang, Taeuk Kim, and H. Kim. “Subgraph-aware training of language models for knowledge graph completion using structure-aware contrastive learning.” In Proceedings of the ACM Web Conference 2025. Association for Computing Machinery, 2025.
  • Kim, Taeuk, H. Jang, J. Yu, Taeuk Kim, H. Lee, J.-H. Im, and S.-W. Kim. “ESPRESSO: An effective approach to passage retrieval for high-quality conversational recommender systems.” Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11942-11949.
  • Jang, Y., J. Kim, H. Kim, E. Lee, Eun-Sol Kim, S. Baek, and J. Yoo. “B-RIGHT: Benchmark re-evaluation for integrity in generalized human-object interaction testing.” In Proceedings of the British Machine Vision Conference (BMVC), 2025.
  • Jung, H., J.-H. Bak, Y. Jeong, G. Lee, J. Ahn, and Eun-Sol Kim. “Zero-shot compositional video learning with coding rate reduction.” In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 20508-20518), 2025.
  • On, S., Y. Jeong, and Eun-Sol Kim. “Structure-guided sequence representation learning for generalizable protein function prediction.” Bioinformatics 41, no. 9 (2025): btaf511.
  • Jang, C., J.-H. Won, S. Jun, C. K. Chung, K. Joo, and Yung-Kyun Noh. “On the information processing of one-dimensional Wasserstein distances with finite samples.” Proceedings of the AAAI Conference on Artificial Intelligence 40 (2026): 22137-22145.
  • Lee, Y.-H., H.-S. Kim, S. Jeon, Q.-S. Auh, I. K. Hong, S. Choi, F. Guastaldi, H. Im, Yung-Kyun Noh, and A. Chaurasia. “Deep learning-based diagnosis of temporomandibular joint osteoarthritis using whole-body bone scans.” iScience 28 (2025): 114027.
  • Park, S., S.-H. Park, J.-S. Oh, S. Hong, K. W. Been, Yung-Kyun Noh, J. K. Hur, and J.-W. Nam. “shRNAI: A deep neural network for the design of highly potent shRNAs.” Molecular Therapy Nucleic Acids 36, no. 4 (2025): 102738.
  • Lee, Y.-H., S. Jeon, D. Kim, Q.-S. Auh, J. H. Lee, and Yung-Kyun Noh. “Clinical and MRI markers for acute vs chronic temporomandibular disorders using a machine learning and deep neural networks.” Communications Medicine 5, no. 1 (2025): 401.
  • Lee, Y.-H., S. Jeon, J. Jung, Q.-S. Auh, Yung-Kyun Noh, et al. “DCNN models with post-hoc interpretability for the automated detection of glossitis and OSCC on the tongue.” Scientific Reports 15 (2025): 31940.
  • Ahn, J. C., Yung-Kyun Noh, M. Hu, X. Shen, V. H. Shah, and P. S. Kamath. “Use of artificial intelligence-generated synthetic data to augment and enhance the performance of clinical prediction models in patients with alcohol-associated hepatitis and acute cholangitis.” Gastro Hep Advances 4, no. 6 (2025): 100643.
  • Jeon, S.-g., J. Yoo, K. Joo, and Eunok Paek. “StrucPTM: A database of structurally validated protein modifications and their conformational variation.” Bioinformatics 42, no. 5 (2026): btag190.
  • Choo, Y., S. Na, and Eunok Paek. “SAPP: Structure aware PTM prediction.” Computers in Biology and Medicine 198 (2025): 111169.
  • Song, K. J., M. Kim, Y. J. Heo, K.-C. Cho, J.-W. Oh, D. H. Kim, C. Hwa, Y. Do, S. Choi, H. S. Hwang, K. Kim, K. Kim, S. Na, Eunok Paek, J.-Y. An, S. J. Jang, M.-S. Kim, and K. P. Kim. “Assessing long-term stored tissues for multi-omics data quality and proteogenomics suitability.” Journal of Proteome Research 24, no. 9 (2025): 4563-4574.
  • Seo, J., S. Choi, and Eunok Paek. “NovoRank: Refinement for de novo peptide sequencing based on spectral clustering and deep learning.” Journal of Proteome Research 24, no. 2 (2025): 903-910.
  • Lee, Sungyoon “Prior forgetting and in-context overfitting.” In Advances in Neural Information Processing Systems (NeurIPS). Curran Associates, 2025.
  • Yoo, H., Sungyoon Lee, C. Jang, D.-S. Han, J. Kim, S. Lim, and B.-T. Zhang. “How classifier features transfer to downstream: An asymptotic analysis in a two-layer model.” In Advances in Neural Information Processing Systems (NeurIPS). Curran Associates, 2025.
  • Hong, J., and Sungyoon Lee. “Variance sensitivity induces attention entropy collapse and instability in transformers.” In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 8360-8378). Association for Computational Linguistics, 2025.
  • Lee, Sungyoon, and Sungyoon Lee. “Prediction risk and estimation risk of the ridgeless least squares estimator under general assumptions on regression errors.” In Proceedings of the International Conference on Learning Representations (ICLR), 2025.
  • Kim, S., E. Choi, and Dong-Kyu Chae. “Causal structure-guided distributionally robust optimization under domain shifts.” Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026.
  • Choi, E., S. Kim, and Dong-Kyu Chae. “Stabilizing causal structure learning under heteroscedasticity: Analysis and mitigation of optimization failures.” Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026.
  • Wasi, A. T., M. R. Islam, M. A. Anik, M. M. Ahsan, T. H. Rafi, and Dong-Kyu Chae. “Position: AI governance needs ISO-like interoperability protocols, not just laws.” Proceedings of the International Conference on Machine Learning (ICML), 2026.
  • Seong, E., H. Lee, D. Kim, C. Kim, and Dong-Kyu Chae. “Emotion-wheel-guided audio-referred text representation for multimodal emotion recognition in conversation.” Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2026.
  • Ridoy, S. Z., A. T. Wasi, K. A. Tonmoy, T. H. Rafi, and Dong-Kyu Chae. “BengaliMoralBench: A benchmark for auditing moral reasoning in LLMs within Bengali language and culture.” Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2026.
  • Anik, M. A., A. T. Wasi, M. M. Topu, T. H. Rafi, and Dong-Kyu Chae. “Generative artificial intelligence for project management: A review of architectures, applications, and governance.” Proceedings of PAKDD 2026, 2026.
  • Wasi, A. T., O. R. Heidari, M. M. U. Anam, A. Agarwal, H. L. Patel, B. Kumar, T. H. Rafi, and Dong-Kyu Chae. “A review of human-centric evaluation of cultural bias in Indic languages within LLMs: Rethinking research directions.” Proceedings of PAKDD 2026, 2026.
  • Zhou, X., Y. Sun, Q. Lu, and Dong-Kyu Chae. “An insect olfactory circuit-inspired neural network for sauce classification.” Proceedings of the IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), 2026.
  • Zhou, X., P. Ye, Dong-Kyu Chae, and Z. Li. “Notice-augmented real-world audit report generation by large-scale complex tabular data understanding and new fields discovery.” Proceedings of DASFAA 2026, 2026.
  • Kim, S., M. Kim, M. Bang, and Dong-Kyu Chae. “Analyzing coarse-to-fine generation of diffusion models through image editing perspectives.” Proceedings of the ACM Symposium on Applied Computing (SAC), 2026.
  • Farooq, M. U., A. U. Rehman, A. Rehman, M. Usman, and Dong-Kyu Chae. “SSMT-Net: A semi-supervised multitask transformer-based network for thyroid nodule segmentation in ultrasound images.” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026.
  • Rafi, T. H., A. Agarwal, H. L. Patel, and Dong-Kyu Chae. “Learning from unknown for open-set test-time adaptation.” Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026.
  • Zhou, X., Z. Li, and Dong-Kyu Chae. “EdgeMTSC: A lightweight large-kernel ConvNet for multivariate time series classification.” Proceedings of the AAAI Conference on Artificial Intelligence 40 (2026): 16531-16539.
  • Nie, J., F. Xie, S. Zhou, X. Zhou, Dong-Kyu Chae, and Z. He. “P2P: Part-to-part motion cues guide a strong tracking framework for LiDAR point clouds.” International Journal of Computer Vision 133, no. 8 (2025): 5326-5342.
  • Farooq, M. U., A. U. Rehman, A. Rehman, M. Usman, and Dong-Kyu Chae. “GDSSA-Net: A gradually deeply supervised self-ensemble attention network for IoMT-integrated thyroid nodule segmentation.” Internet of Things 31 (2025): 101598.
  • Kim, H., E. Seong, H. Lee, Dong-Kyu Chae, and S. Lee. “Decoding multiculturalism through linguistic landscapes: A deep learning-based OCR analysis of street view images.” Urban Informatics 4 (2025): 5.
  • Zhou, X., Q. Lu, and Dong-Kyu Chae. “"Where does this strange smell come from?": Enabling conversational interfaces for artificial olfaction.” In Findings of the Association for Computational Linguistics: EMNLP 2025. Association for Computational Linguistics, 2025
  • Lee, J., H. Shin, N. Kim, Dong-Kyu Chae, M. Cha, and C. Park. “Waypoint POI recommendation for vehicle navigation services using hierarchical graphs and contrastive learning.” In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM) (pp. 5805-5812). ACM, 2025.
  • Rafi, T. H., A. Agarwal, H. L. Patel, and Dong-Kyu Chae. “Towards robust continual test-time adaptation via neighbor filtration.” Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), 2025.
  • Faisal, W., A. T. Wasi, D. Sharma, M. A. Anik, T. H. Rafi, and Dong-Kyu Chae. “Knowledge Explorer: An agentic AI framework for interactive, personalized and multilingual learning experience.” Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2025.
  • Wasi, A. T., M. R. Islam, A. Rahman, T. Yeasmin, A. Agarwal, H. L. Patel, T. H. Rafi, and Dong-Kyu Chae. “LegalMind: An intelligent solution for legal document analysis with user-centric UI and AI-driven capabilities in mobile devices.” Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2025.
  • Wasi, A. T., R. Islam, M. R. Islam, F. Y. Sadeque, T. H. Rafi, and Dong-Kyu Chae. “Exploring Bengali creative storytelling capabilities of large language models across cultural variations.” Proceedings of the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), 2025.
  • Zhou, X., Q. Lu, and Dong-Kyu Chae. “What if LLMs can smell: A prototype.” In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (pp. 11141-11144), 2025.
  • Agarwal, A., S. Panda, A. Charles, H. L. Patel, B. Kumar, P. Pattnayak, T. H. Rafi, T. Kumar, H. Meghwani, K. Gupta, and Dong-Kyu Chae. “MVTamperBench: Evaluating robustness of vision-language models.” In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 18804-18828). Association for Computational Linguistics, 2025.
  • Li, X., X. Zhou, Dong-Kyu Chae, and S. Lee. “CityAPIBench: A benchmark of domain-adapted large language models for urban big data query.” Proceedings of CUPUM 2025, 2025.
  • Na, Y., D. Kim, and Dong-Kyu Chae. “DialRet: Enhancing dialogue retention for multi-session conversations.” In Advances in Knowledge Discovery and Data Mining (PAKDD 2025) (pp. 78-89). Springer, 2025.
  • Park, B., H. Lee, S. Kim, and Dong-Kyu Chae. “Dynamic spuriosity bias harmonizer: Combatting spurious features for domain generalization.” In Advances in Knowledge Discovery and Data Mining (PAKDD 2025) (pp. 44-56). Springer, 2025.
  • Kim, S., M. Kim, and Dong-Kyu Chae. “A unified detector for both adversarial attacks and out-of-distribution samples based on kernel path distribution.” In Advances in Knowledge Discovery and Data Mining (PAKDD 2025). Springer, 2025.
  • Park, B., H. Lee, S. Kim, and Dong-Kyu Chae. “Parameter efficient tuning for graph neural networks via a weight adapter module.” In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vol. 1. Springer, 2025.
  • Wasi, A. T., T. H. Rafi, and Dong-Kyu Chae. “Gaussian regularization in neural graph learning.” Proceedings of DASFAA 2025, 2025.
    Rafi, T. H., K. Serbetar, A. Agarwal, H. L. Patel, B. Kumar, and Dong-Kyu Chae. “Instance-aware test-time adaptation for domain generalization.” Proceedings of DASFAA 2025, 2025.
  • Bit, F. L., I. K. binti Zamri, A. T. Wasi, T. H. Rafi, and Dong-Kyu Chae. “Mitigating linguistic bias between Malay and Indonesian languages using masked language models.” Proceedings of DASFAA 2025, 2025.
  • Patel, H. L., A. Agarwal, A. Das, B. Kumar, S. Panda, P. Pattnayak, T. H. Rafi, T. Kumar, and Dong-Kyu Chae. “SweEval: Do LLMs really swear?.” A safety benchmark for testing limits for enterprise use. In Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): Industry Track (pp. 558-582). Association for Computational Linguistics, 2025.
  • Wasi, A. T., R. Islam, M. R. Islam, F. Y. Sadeque, T. H. Rafi, and Dong-Kyu Chae. “Dialectal bias in Bengali: An evaluation of multilingual large language models across cultural variations.” In Companion Proceedings of the ACM Web Conference 2025 (pp. 1380-1384). Association for Computing Machinery, 2025.
  • Kim, K., E. Jeong, S. Lee, S. Yoon, and Yong Suk Choi. “CLAWS: Creativity detection for LLM-generated solutions using attention window of sections.” In Advances in Neural Information Processing Systems (NeurIPS). Curran Associates, 2025.
  • Kim, S., S. J. Hwang, J. Kim, J. Park, and Yong Suk Choi. “ReGraphRAG: Reorganizing fragmented knowledge graphs for multi-perspective retrieval-augmented generation.” In Findings of the Association for Computational Linguistics: EMNLP 2025. Association for Computational Linguistics, 2025.
  • Kang, M., and Yong Suk Choi. “FreeMix: Personalized structure and appearance control without finetuning.” Applied Sciences 15, no. 18 (2025): 9889.
  • Lee, D., H. Jung, and Yong Suk Choi. “Mind the link: Discourse link-aware hallucination detection in summarization.” Applied Sciences 15, no. 19 (2025): 10506.
  • Kim, S., and Yong Suk Choi. “Contextual feature expansion with superordinate concept for compositional zero-shot learning.” Applied Sciences 15, no. 17 (2025): 9837.
  • Kim, S., and Yong Suk Choi. “Predict unmatching compositions for compositional zero-shot learning.” IEEE Access (2025).
  • Jeong, E., and Yong Suk Choi. “ACR: Adaptive confidence re-scoring for reliable answer selection among multiple candidates.” Applied Sciences 15, no. 17 (2025): 9587.
  • Seo, H., and Yong Suk Choi. “V-PRUNE: Semantic-aware patch pruning before tokenization in vision-language model inference.” Applied Sciences 15, no. 17 (2025): 9463.
  • Kang, M., and Yong Suk Choi. “Editable noise map inversion: Encoding target-image into noise for high-fidelity image manipulation.” In Proceedings of the International Conference on Machine Learning (ICML). PMLR, 2025.
  • Jeong, Y., and Yong Suk Choi. “NuCap: A numerically aware captioning framework for improved numerical reasoning.” Applied Sciences 15, no. 10 (2025): 5608.
  • Kim, K., and Yong Suk Choi. “CFP-AL: Combining model features and prediction for active learning in sentence classification.” Applied Sciences 15, no. 1 (2025): 482.
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