Authors: Derek Jacoby, Tianyi Zhang, Aanchan Mohan, Yvonne Coady

Published on: April 11, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2404.16053

Summary

• What is new: Addressing the issue of response time in LLM-driven spoken dialogues by generating responses before the speaker completes their utterance to match human dialogues’ latency.
• Why this is important: The delay in response times of LLM-driven spoken dialogues does not match the quick turnarounds seen in human conversations.
• What the research proposes: A method that allows for understanding and responding to an utterance in real-time, even if it means missing out on the tail end of the speaker’s utterance.
• Results: Using GPT-4 and the Google NaturalQuestions database, it was found that missing context from the end of a question could be filled in effectively over 60% of the time.

Technical Details

Technological frameworks used: Groq for processing LLMs, Google NaturalQuestions (NQ) database for data

Models used: GPT-4

Data used: Google NaturalQuestions (NQ)

Potential Impact

This could impact customer service technologies, virtual assistants, and chatbot providers by improving response times and making interactions more natural.

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