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

arXiv: https://arxiv.org/abs/2102.03062 · HTML: https://mlsanity.com/paper/2102.03062/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [Natural Language Processing for Information Extraction](https://mlsanity.com/paper/1807.02383/) (2018)
- [Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network](https://mlsanity.com/paper/2007.04248/) (2020)
- [A Maturity Assessment Framework for Conversational AI Development Platforms](https://mlsanity.com/paper/2012.11976/) (2020)

## Built on by (most cited first, 10 of 10)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions](https://mlsanity.com/paper/2305.10435/) (2023)
- [Multilingual Language Models are not Multicultural: A Case Study in Emotion](https://mlsanity.com/paper/2307.01370/) (2023)
- [CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology](https://mlsanity.com/paper/2402.01411/) (2024)
- [Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis](https://mlsanity.com/paper/2402.01386/) (2024)
- [Autonomous Agents in Software Development: A Vision Paper](https://mlsanity.com/paper/2311.18440/) (2023)
- [Improving Short Text Classification With Augmented Data Using GPT-3](https://mlsanity.com/paper/2205.10981/) (2022)
- [From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence](https://mlsanity.com/paper/2308.02448/) (2023)

## Abstract (arXiv)

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.