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Ever since the development of GPT-3 in the natural language processing (NLP)field, in-context learning (ICL) has played an essential role in utilizinglarge language models (LLMs). By presenting the LM utterance-labeldemonstrations at the input, the LM can accomplish few-shot learning withoutrelying on gradient descent or requiring explicit modification of itsparameters. This enables the LM to perform various downstream tasks in ablack-box manner. Despite the success of ICL in NLP, little work is exploringthe possibility of ICL in speech processing. This study is the first workexploring ICL for speech classification tasks with textless speech LM. We firstshow that the current speech LM lacks the ICL capability. We then performwarmup training on the speech LM, equipping the LM with demonstration learningcapability. This paper explores and proposes the first speech LM capable ofperforming unseen classification tasks in an ICL manner.

Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks