Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks
It addresses the problem of adapting in-context learning from NLP to speech processing for researchers and practitioners, but it is incremental as it builds on existing NLP techniques.
This study tackled the lack of in-context learning capability in speech language models for speech classification tasks by proposing a warmup training method, enabling the model to perform unseen classification tasks without gradient descent or parameter modifications.
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.