Understanding Emails and Drafting Responses - An Approach Using GPT-3

By Jonas Thiergart, Stefan Huber, Thomas Übellacker. 2021

30 citations recorded by the source corpus.

Providing computer systems with the ability to understand and generate natural language has long been a challenge of engineers. Recent progress in natural language processing (NLP), like the GPT-3 language model released by OpenAI, has made both possible to an extent. In this paper, we explore the possibility of rationalising email communication using GPT-3. First, we demonstrate the technical feasibility of understanding incoming emails and generating responses, drawing on literature from the disciplines of software engineering as well as data science. Second, we apply knowledge from both business studies and, again, software engineering to identify ways to tackle challenges we encountered. Third, we argue for the economic viability of such a solution by analysing costs and market demand. We conclude that applying GPT-3 to rationalising email communication is feasible both technically and economically.

Subfields: Large Language Models, Natural Language Generation

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  1. Thippa Reddy Gadekallu
  2. Praveen Kumar Reddy Maddikunta
  3. Gokul Yenduri
  4. R. Jhaveri
  5. B. Prabadevi
  6. M. Ramalingam
  7. Athanasios V. Vasilakos
  8. Weizheng Wang

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