TELLM: Advancements in Knowledge Incorporation and Task-specific Enhancements of Large Language Models

Customer service is crucial for any business to maintain good customer relationships and growth. However, addressing a wide variety of customer inquiries often requires deep domain expertise that may not be readily available. This paper presents TELLM, a customer service AI agent leveraging large la...

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Bibliographic Details
Published inIranian Conference on Electrical Engineering pp. 1 - 5
Main Authors Feizi, Fatemeh, HosseinNia, Amirhossein, Hemmatyar, MohammadMahdi, Rahimi, Fatemeh, Kaleibar, Farhoud Jafari
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.05.2024
Subjects
Customer services
Deep Learning
Knowledge based systems
Large language models
Measurement
Mission critical systems
Question-answering
Reinforcement learning
Subject matter experts
Telecom
Online AccessGet full text
ISSN2642-9527
DOI10.1109/ICEE63041.2024.10667786

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Summary:Customer service is crucial for any business to maintain good customer relationships and growth. However, addressing a wide variety of customer inquiries often requires deep domain expertise that may not be readily available. This paper presents TELLM, a customer service AI agent leveraging large language models to provide technical support for telecom companies. TELLM is trained using a knowledge base containing categorized technical solutions through dual-phase fine-tuning. It is further refined through reinforcement learning with feedback from subject matter experts. Evaluation on a real-world customer service dataset demonstrates TELLM outperforms prior approaches on automatic metrics and achieves high scores in human evaluation for response accuracy, clarity and effectiveness.
ISSN:2642-9527
DOI:10.1109/ICEE63041.2024.10667786