  Ever since the development of GPT-3 in the natural language processing (NLP)
field, in-context learning (ICL) has played an essential role in utilizing
large language models (LLMs). By presenting the LM utterance-label
demonstrations at the input, the LM can accomplish few-shot learning without
relying on gradient descent or requiring explicit modification of its
parameters. This enables the LM to perform various downstream tasks in a
black-box manner. Despite the success of ICL in NLP, little work is exploring
the possibility of ICL in speech processing. This study is the first work
exploring ICL for speech classification tasks with textless speech LM. We first
show that the current speech LM lacks the ICL capability. We then perform
warmup training on the speech LM, equipping the LM with demonstration learning
capability. This paper explores and proposes the first speech LM capable of
performing unseen classification tasks in an ICL manner.
