3 papers
cs.CL2026
What Shapes Brain Alignment of Large Language Models during Dialogue? Insights from Language Production and Comprehension
Yuko Nakagi, Yuya Chiba, Yasue Kishino
Brain alignment of large language models (LLMs) offers a way to investigate representational similarities between LLMs and the human brain during language processing. While previou…
cs.CL2026
M3-DuplexBench: A Multi-Turn, Multilingual, Multidomain Benchmark for Full-Duplex Spoken Dialogue Models
Ryo Fukuda, Atsushi Ando, Hiroki Kanagawa +4
Full-duplex spoken dialogue systems (FDSDSs) can listen while speaking, enabling natural conversational behaviors such as smooth turn-taking, backchannel handling, and user barge-i…
cs.CL2026
Evaluating Large Language Models Abilities for Addressee, Turn-change, and Next Speaker Prediction in Meetings
Ryo Fukuda, Takatomo Kano, Siddhant Arora +7
We investigate turn-taking in multimodal multi-party conversations using large language models (LLMs). We construct an evaluation framework for three tasks: addressee detection, tu…