Prof. Kurohashi was selected as ACL Fellows 2025. †

We will present the following paper at AAAI 2026 (2026/1). †

• Yang Liu, Masahiro Kaneko, Chenhui Chu:
  On the Alignment of Large Language Models with Global Human Opinion

The following presentations and roles are scheduled for AACL 2025 (2025/12) †

• Professor Kurohashi will deliver the keynote address.
• Shimizu, a doctoral student, will chair the Student Research Workshop.
• We will present the following paper.
  • Kunal Kingkar Das, Manoj Balaji Jagadeeshan, Nallani Chakravartula Sahith, Jivnesh Sandhan, and Pawan Goyal: Still Not There: Can LLMs Outperform Smaller Task-Specific Seq2Seq Models on the Poetry-to-Prose Conversion Task?
  • Wan Jou She, Lis Pereira, Fei Cheng, Sakiko Yahata, Panote Siriaraya, Eiji Aramaki. EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels

We will present the following papers at EMNLP 2025 (2025/11). †

• Zhengdong Yang, Zhen Wan, Sheng Li, Chao-Han Huck Yang, Chenhui Chu:
  CoVoGER: A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models
• Yoshiki Takenami, Yin Jou Huang, Yugo Murawaki, Chenhui Chu:
  How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations (Findings)
• Yang Liu, Chenhui Chu:
  Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs (Findings)
• Jivnesh Sandhan, Fei Cheng, Tushar Sandhan, and Yugo Murawaki:
  From Disney-World to Reality: A Context-Dependent Testbed for Personality Assessment of Large Language Models (Findings)
• Ruiyi Yan and Yugo Murawaki:
  Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models.
• Yin Jou Huang, Rafik Hadfi:
  Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models (Findings)
• Sakiko Yahata, Zhen Wan, Fei Cheng, Sadao Kurohashi, Hisahiko Sato, Ryozo Nagai: Causal Tree Extraction from Medical Case Reports:
  A Novel Task for Experts-like Text Comprehension
• Kazuma Kobayashi, Zhen Wan, Fei Cheng, Yuma Tsuta, Xin Zhao, Junfeng Jiang, Jiahao Huang, Zhiyi Huang, Yusuke Oda, Rio Yokota, Yuki Arase, Daisuke Kawahara, Akiko Aizawa, Sadao Kurohashi:
  Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training (Findings)

Search Engine Infrastructure based on Deep Natural Language Processing †

The essential purpose of information retrieval is not to retrieve just a relevant document but to acquire the information or knowledge in the document. We have been developing a next-generation infrastructure of information retrieval on the basis of the following techniques of deep natural language processing: precise processing based not on words but on predicate-argument structures, identifying the variety of linguistic expressions and providing a bird's-eye view of search results via clustering and interaction.

Machine Translation †

To bring automatic translation by computers to the level of human translation, we have been studying next-generation methodology of machine translation on the basis of text understanding and a large collection of translation examples. We have already accomplished practical translation on the domain of travel conversation, and constructed a translation-aid system that can be used by experts of patent translation.