Spoken Question-Answering (SQA) is a core capability for useful and interactive artificial intelligence systems. Recently, several speech-language models (SpeechLMs) have been released with a specific focus on improving their SQA performance. However, a lack of controlled ablations of pretraining data processing and curation makes it challenging to understand what factors account for performance, despite substantial gains from similar studies in other data modalities. In this work, we address this gap by conducting a data-centric exploration for pretraining SpeechLMs. We focus on three… Read More
Data-Centric Lessons To Improve Speech-Language Pretraining
Spoken Question-Answering (SQA) is a core capability for useful and interactive artificial intelligence systems. Recently, several speech-language models (SpeechLMs) have been released with a specific focus on improving their SQA performance. However, a lack of controlled ablations of pretraining data processing and curation makes it challenging to understand what factors account for performance, despite substantial gains from similar studies in other data modalities. In this work, we address this gap by conducting a data-centric exploration for pretraining SpeechLMs. We focus on three…
Related Posts
The philosophical puzzle of rational artificial intelligence
As AI technology advances, a new interdisciplinary course seeks to equip students with foundational critical…
Method teaches generative AI models to locate personalized objects
After being trained with this technique, vision-language models can better identify a unique item in…
Top 5 Agentic AI LLM Models
In 2025, “using AI” no longer just means chatting with a model, and you’ve probably…