March 31, 2024, noon | code_your_own_AI

code_your_own_AI www.youtube.com

A cutting-edge approach to addressing consistency in large language models (LLMs), a challenge crucial for AI systems, especially in customer service interactions. The central concern is to ensure that AI chatbots can uniformly understand and respond to various customer inquiries that differ in phrasing but are identical in intent.

The innovative solution proposed involves two key training methods:

1. Instruction Augmented Supervised Fine-Tuning (SFT(IA)): This method enriches the training dataset with paraphrased versions of task instructions, using models like Vicuna, …

ai chatbots ai systems alignment challenge chatbots customer customer service edge interactions language language models large language large language models llm llms service solution systems

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Tableau/PowerBI Developer (A.Con)

@ KPMG India | Bengaluru, Karnataka, India

Software Engineer, Backend - Data Platform (Big Data Infra)

@ Benchling | San Francisco, CA