1 citations · 2 across the 11 of their papers we have counts for
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TriggerBench: Investigating Prospective Memory for Large Language Models
Tianhua Zhang, Xinjiang Wang, Qianxi Zhang +6
While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prosp…
Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs
Dingdong Wang, Junan Li, Mingyu Cui +3
With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has d…
MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark
Dingdong Wang, Junan Li, Jincenzi Wu +4
Speech inherently contains rich acoustic information that extends far beyond the textual language. In real-world spoken language understanding, effective interpretation often requi…
A Comparative Study of Discrete Speech Tokens for Semantic-Related Tasks with Large Language Models
Dingdong Wang, Mingyu Cui, Dongchao Yang +2
With the rise of Speech Large Language Models (Speech LLMs), there has been growing interest in discrete speech tokens for their ability to integrate with text-based tokens seamles…