Observations on LLMs for Telecom Domain: Capabilities and Limitations
May 22, 2023 Β· Declared Dead Β· π International Conference on AI-ML-Systems
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Authors
Sumit Soman, Ranjani H G
arXiv ID
2305.13102
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CL,
cs.IR,
cs.LG
Citations
31
Venue
International Conference on AI-ML-Systems
Last Checked
4 months ago
Abstract
The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In this paper, we analyze capabilities and limitations of incorporating such models in conversational interfaces for the telecommunication domain, specifically for enterprise wireless products and services. Using Cradlepoint's publicly available data for our experiments, we present a comparative analysis of the responses from such models for multiple use-cases including domain adaptation for terminology and product taxonomy, context continuity, robustness to input perturbations and errors. We believe this evaluation would provide useful insights to data scientists engaged in building customized conversational interfaces for domain-specific requirements.
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