Ananiadou, S. - NaCTeM Publications - Aigaion 2.0

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Nachname(n):	Ananiadou

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Xie, Q., Han, W., Chen, Z., Xiang, R., Zhang, X., He, Y., Xiao, M., Li, D., Dai, Y., Feng, D., Xu, Y., Kang, H., Kuang, Z., Yuan, C., Yang, K, Luo, Z., Zhang, T., Liu, Z., Xiong, G., Deng, Z., Jiang, Y., Yao, Z., Li, H., Yu, Y., Lopez-Lira, A., Wang, B., Lai, Y., Wang, H., Peng, M., Ananiadou, S. und Huang, J., FinBen: An Holistic Financial Benchmark for Large Language Models, in: Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

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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. und 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, Seiten 119- 126, 2024

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Hu, G., Qin, K., Yuan, C., Peng, M., Lopez-Lira, A., Wang, B., Ananiadou, S., Huang, J. und Xie, Q., No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks, arXiv, 2024

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Luo, Z., Xie, Q. und Ananiadou, S., The Lay Person’s Guide to Biomedicine: Orchestrating Large Language Models, arXiv, 2024

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Thompson, P., Ananiadou, S., Basinas, I., Brinchmann, B. C., Cramer, C., Galea, K. S., Ge, C., Georgiadis, P., Kirkeleit, J., Kuijpers, E., Nguyen, N. T. H., Nuñez, R., Schlünssen, V., Stokholm, Z. A., Taher, E. A., Tinnerberg, H., van Tongeren, M. und Xie, Q., Supporting the working life exposome: Annotating occupational exposure for enhanced literature search (2024), in: PLoS ONE, 19:8(e0307844)

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Liu, Z., Liu, B, Thompson, P., Yang, K und 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), Seiten 4649 - 4656, 2024

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Zhang, T., Yang, K, Ji, S., Liu, B, Xie, Q. und Ananiadou, S., SuicidEmoji: Derived Emoji Dataset and Tasks for Suicide-Related Social Content, in: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '24), Seiten 1136 - 1141, 2024

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Ji, S., Zhang, T., Yang, K und Ananiadou, S., Rethinking large language models in mental health applications, arXiv, 2023

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Zhang, X., Xiang, R., Yuan, C., Feng, D., Han, W., Lopez-Lira, A., Liu, X. -Y., Ananiadou, S., Peng, M., Huang, J. und 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), Seiten 6236-6246, 2024

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Yu, Z. und 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, Seiten 3281–3292, 2024

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Liu, Z., Yang, K, Zhang, T., Xie, Q., Yu, Z. und Ananiadou, S., EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis, in: Proceedings of KDD 2024, Seiten 5487 - 5496, 2024

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Hua, Y., Liu, F., Yang, K, Li, Z., Sheu, Y., Zhou, P., Moran, L. V., Ananiadou, S. und Beam, A., Large Language Models in Mental Health Care: a Scoping Review, arXiv, 2024

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Liu, B, Schlegel, V., Batista-Navarro, R. und Ananiadou, S., Argument mining as a multi-hop generative machine reading comprehension task, in: Findings of the Association for Computational Linguistics: EMNLP 2023, Seiten 10846–10858, 2023

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Liu, Z., Zhang, T., Yang, K, Thompson, P., Yu, Z. und Ananiadou, S., Emotion detection for misinformation: A review (2024), in: Information Fusion, 107(102300)

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Yang, K, Zhang, T., Kuang, Z., Xie, Q., Huang, J. und Ananiadou, S., MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models, in: Proceedings of WWW 2024, Seiten 4489 - 4500, 2024

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Yuan, C., Xie, Q. und Ananiadou, S., Temporal relation extraction with contrastive prototypical sampling (2024), in: Knowledge-based Systems, 286(111410)

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Dwivedi, Y. K., Hughes, L., Bhadeshia, H. K. D. H., Ananiadou, S., Cohn, A. G., Cole, J. M., Conduit, G. J., Desarkar, M. S. und Wang, X., Artificial intelligence (AI) futures: India-UK collaborations emerging from the 4th Royal Society Yusuf Hamied workshop (2024), in: International Journal of Information Management(102725)

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Yuan, C., Xie, Q., Huang, J. und 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), Seiten 1963 - 1974, 2024

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Bishop, J., Xie, Q. und Ananiadou, S., LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation, in: Proceedings of LREC-COLING 2024, Seiten 10777-10789, 2024

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Liu, B, Schlegel, V., Thompson, P., Batista-Navarro, R. und Ananiadou, S., Global information-aware argument mining based on a top-down multi-turn QA model (2023), in: Information Processing & Management, 60:5(103445)

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Xie, Q., Tiwari, P. und Ananiadou, S., Knowledge-enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization (2023), in: IEEE Journal of Biomedical and Health Informatics

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Min, E. und Ananiadou, S., PESTO: A Post-User Fusion Network for Rumour Detection on Social Media, in: Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, Seiten 1-10, 2023

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Yang, K, Zhang, T. und Ananiadou, S., Disentangled Variational Autoencoder for Emotion Recognition in Conversations (2023), in: IEEE Transactions on Affective Computing(1-12)

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Ji, S., Zhang, T., Yang, K und Ananiadou, S., Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health, arXiv, 2023

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Xie, Q., Luo, Z. und Ananiadou, S., A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models, arXiv, 2023

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