3 papers
cs.AI2026
SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Anticipation?
Yueyi Yang, Haotian Liu, Fang Kang +4
We explore the use of large language models (LLMs) for next-utterance anticipation in human dialogue. Despite recent advances in LLMs demonstrating their ability to engage in natur…
cs.CL2026
A framework for analyzing concept representations in neural models
Burin Naowarat, Hao Tang, Sharon Goldwater
Understanding how neural models represent human-interpretable concepts is challenging. Prior work has explored linear concept subspaces from diverse perspectives, such as probing a…
cs.SD2025
Effective Context in Neural Speech Models
Yen Meng, Sharon Goldwater, Hao Tang
Modern neural speech models benefit from having longer context, and many approaches have been proposed to increase the maximum context a model can use. However, few have attempted…