Alle Publicaties gesorteerd op tijdschrift en type
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Alle publicaties van type Incollection


2004

Spasić, I., Nenadić, G. en Ananiadou, S., Learning to Classify Biomedical Terms through Literature Mining and Genetic Algorithms, in: Intelligent Data Engineering and Automated Learning – IDEAL 2004, pagina's 345--351, Springer-Verlag, 2004
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Alle publicaties van type Inproceedings


In Press

Yu, Z., Belinkov, Y. en Ananiadou, S., Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models, in: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), In Press
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Kabir, M., Abrar, A. en Ananiadou, S., Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs, in: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), In Press
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Liu, Z., Thompson, P., Rong, J. en Ananiadou, S., ConspEmoLLM-v2: A robust and stable model to detect sentiment-transformed conspiracy theories, in: Proceedings of the 14th Conference on Prestigious Applications of Intelligent Systems (PAIS-2025), In Press
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Kabir, M., Tahsin, T. en Ananiadou, S., From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modelin, in: Findings of the Association for Computational Linguistics: EMNLP 2025, In Press
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Yu, Z. en Ananiadou, S., Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs, in: Findings of the Association for Computational Linguistics: EMNLP 2024, In Press
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Peng, X., Papadopoulos, T., Soufleri, E., Giannouris, P., Xiang, R., Wang, Y., Qian, L., Huang, J., Xie, Q. en Ananiadou, S., Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance, in: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), In Press
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Yang, K, Liu, Z., Xie, Q., Huang, J., Min, E. en Ananiadou, S., Selective Preference Optimization via Token-Level Reward Function Estimation, in: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), In Press
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Zhang, X., Wei, Q., Zhu, Y., Wu, F. en Ananiadou, S., THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction, in: Findings of the Association for Computational Linguistics: EMNLP 2024, In Press
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2025

Yano, K., Luo, Z., Huang, J., Xie, Q., Asada, M., Yuan, C., Yang, K, Miwa, M., Ananiadou, S. en Tsujii, J., ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain, in: Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025), pagina's 4670–4688, 2025
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Luo, Z., Yuan, C., Xie, Q. en Ananiadou, S., EMPEC: A Comprehensive Benchmark for Evaluating Large Language Models Across Diverse Healthcare Professions, in: Findings of the Association for Computational Linguistics: ACL 2025, pagina's 9945–9958, 2025
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Soufleri, E. en Ananiadou, S., Enhancing Stress Detection on Social Media Through Multi-Modal Fusion of Text and Synthesized Visuals, in: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP), pagina's 34–43, 2025
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Liu, Z., Wang, K., Bao, Z., Zhang, X., Dong, J., Yang, K, Kabir, M., Giannouris, P., Xing, R., Park, S., Kim, J., Li, D., Xie, Q. en Ananiadou, S., FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task, in: Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pagina's 271–276, 2025
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Zhang, X., Wei, Q., Zhu, Y., Zhang, L., Zhou, D. en Ananiadou, S., SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling, in: Findings of the Association for Computational Linguistics: ACL 2025, pagina's 16338–16356, 2025
[DOI]
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2024

Yuan, C., Xie, Q., Huang, J. en Ananiadou, S., Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models, in: Proceedings of the ACM on Web Conference 2024 (WWW '24), pagina's 1963 - 1974, 2024
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Liu, Z., Liu, B, Thompson, P., Yang, K en Ananiadou, S., ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model, in: Proceedings of the 13th International Conference on Prestigious Applications of Intelligent Systems (PAIS-2024), pagina's 4649 - 4656, 2024
[DOI]
[URL]
Zhang, X., Xiang, R., Yuan, C., Feng, D., Han, W., Lopez-Lira, A., Liu, X. -Y., Ananiadou, S., Peng, M., Huang, J. en Xie, Q., Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English, in: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), pagina's 6236-6246, 2024
[DOI]
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Xie, Q., Huang, J., Li, D., Chen, Z., Xiang, R., Xiao, M., Yu, Y., Somasundaram, V., Yang, K, Yuan, C., Luo, Z., Liu, Z., He, Y., Jiang, Y., Li, H., Feng, D., Liu, X. -Y., Wang, B., Wang, H., Lai, Y., Suchow, J., Lopez-Lira, A., Peng, M. en Ananiadou, S., FinNLP-AgentScen-2024 Shared Task: Financial Challenges in Large Language Models - FinLLMs, in: Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for Scenario Planning, pagina's 119- 126, 2024
[URL]
Wang, Y., Feng, D., Dai, Y., Chen, Z., Huang, J., Ananiadou, S., Xie, Q. en Wang, H., HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection, in: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
[URL]
Yu, Z. en Ananiadou, S., How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning, in: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pagina's 3281–3292, 2024
[DOI]
[URL]
Yu, Z. en Ananiadou, S., Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis, in: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pagina's 3293–3306, 2024
[DOI]
[URL]
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