In the digital era of emerging technologies, LLMs have emerged as a powerful tool revolutionizing many aspects of human society and culture, reshaping how we interact with computers. Yet, there is a pivotal challenge that needs to be solved. The limitations of  LLMs are evident, revealing a gap in the inability to grasp the contexts and nuances of a conversation and depend on the quality and specificity of the prompt. One major limitation is they lack the depth of real communication, missing all the paralinguistic information.

Project Rumi from Microsoft aims to enhance the capabilities of LLMs by addressing limitations in understanding nonverbal cues and contextual nuances. It incorporates paralinguistic input into prompt-based interactions with LLMs to improve the quality of communication. The researchers have used audio and video models to detect real-time non-verbal cues from data streams. Two separate models are used for paralinguistic information from the user’s audio, the first prosody tone and inflection of audio and the other from the semantics of the speech. They have used vision transformers for encoding the frames and identifying facial expressions from video. A downstream service incorporates the paralinguistic information into the text-based prompt. This multimodal approach aims to enhance user sentiment and intent understanding, thus elevating human-AI interaction to a new level.

In this research, researchers have only briefly explored the role that paralinguistic provides in communicating critical information about user’s intentions. In the future, they plan to model to make the model better and more efficient. They also want to add more details like  HRV (heart rate variability) derived from standard video and cognitive and ambient sensing. This is all part of a bigger effort to add unspoken meaning and intention in the next wave of interactions with AI.

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