[dbjapan] [CfP] 論文募集中： Information Processing & Management (IP&M) (IF: 7.4) 特集号タイトル： Causal Reasoning in Language Models

MLの皆様 ＊重複して受け取られた場合はご容赦ください／Apologies for cross-posting＊ 北見工業大学のプタシンスキと申します． 現在，Information Processing＆Management（IP＆M）（IF：7.4）ジャーナルにて「Special Issue on Causal Reasoning in Language Models」（言語モデルにおける因果推論）という特集号のため論文募集を行っております． 原稿の提出締め切りは2025年3月31日ですが，論文は投稿後すぐに査読に送られ，採択された場合すぐに公開となります． 論文の投稿をどうぞご検討ください。 https://www.sciencedirect.com/journal/information-processing-and-management… どうぞよろしくお願いいたします． ミハウ・プタシンスキ（博士／情報科学，准教授） テキスト情報処理研究室 北見工業大学 〒090-8507 北見市公園町165番地 TEL/FAX: 0157-26-9327 michal(a)mail.kitami-it.ac.jp ============================================ Journal: Information Processing & Management (Impact Factor: 7.4) Special Issue on "Causal Reasoning in Language Models" Guest Editors: - Michal Ptaszynski (Kitami Institute of Technology), michal(a)mail.kitami-it.ac.jp - Rafal Rzepka (Hokkaido University) - Rafal Urbaniak (University of Ghent) Introduction: Causal reasoning is a fundamental cognitive ability that allows humans to understand the cause-and-effect relationships in the world around them. Integrating causal reasoning capabilities into language models has emerged as a promising research direction, with significant implications for natural language processing (NLP) and artificial intelligence (AI) applications. The special issue on "Causal Reasoning in Language Models" aims to provide a platform for researchers to explore the latest advancements and challenges in this burgeoning field. Topics of Interest: We invite submissions on a wide range of topics related to causal reasoning in language models, including but not limited to: - Causal inference techniques in natural language processing - Evaluating causal understanding in large language models - Causal representations in transformer architectures - Counterfactual reasoning capabilities of language models - Causal discovery from unstructured text data - Incorporating causal knowledge into language model pre-training - Causal explanation generation using language models - Bias and fairness in causal language modeling - Causal reasoning for improved few-shot and zero-shot learning - Temporal and event causal reasoning in language models - Theoretical frameworks for representing causal knowledge in language models - Methodologies for incorporating causal reasoning into NLP tasks, such as text generation, question answering, and summarization - Evaluation metrics and benchmarks for assessing the performance of causal reasoning models in language understanding tasks - Applications of causal reasoning in real-world scenarios, including healthcare, finance, social media analysis, and more - Ethical considerations and societal implications of integrating causal reasoning into AI systems - Interdisciplinary approaches that combine insights from linguistics, cognitive science, and computer science to advance causal reasoning in language models Submission Guidelines: Papers submitted to this special issue must adhere to the submission guidelines of Information Processing & Management. Manuscripts should be original, unpublished works not currently under review elsewhere. All submissions will undergo a rigorous peer review process to ensure high quality and relevance to the special issue. Important Dates: - Submission opens: 2024-7-31 - Submission closes: 2025-3-31 Submission Instructions: Submit your manuscript to the Special Issue category (VSI: CAUSAL LLMs) through the online submission system of Information Processing & Management (https://www.editorialmanager.com/ipm/default.aspx). All the submissions should follow the general author guidelines of Information Processing & Management (https://www.sciencedirect.com/journal/information-processing-and-management). For any inquiries or further information, please contact the Managing Guest Editor at michal(a)mail.kitami-it.ac.jp. Conclusion: We encourage researchers from academia and industry to contribute their latest findings and innovations to this special issue. By bringing together a collection of high-quality papers on causal reasoning in language models, we aim to advance the state of the art in NLP, foster interdisciplinary collaborations, and pave the way for future developments in AI. We look forward to your contributions. Sincerely, Michal Ptaszynski, in the name of all Guest Editors ============================================