# Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippets

arXiv: https://arxiv.org/abs/2301.03373 · HTML: https://mlsanity.com/paper/2301.03373/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reasoning with Language Model Prompting: A Survey](https://mlsanity.com/paper/2212.09597/) (2022)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Few-Shot Semantic Parsing with Language Models Trained On Code](https://mlsanity.com/paper/2112.08696/) (2021)
- [Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs](https://mlsanity.com/paper/2111.03922/) (2021)
- [Codex Hacks HackerRank: Memorization Issues and a Framework for Code Synthesis Evaluation](https://mlsanity.com/paper/2212.02684/) (2022)
- ["It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools](https://mlsanity.com/paper/2212.03491/) (2022)
- [COBOL to Java and Newspapers Still Get Delivered](https://mlsanity.com/paper/1808.03724/) (2018)

## Built on by (most cited first, 3 of 3)

- [Numeracy from Literacy: Data Science as an Emergent Skill from Large Language Models](https://mlsanity.com/paper/2301.13382/) (2023)
- [CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit](https://mlsanity.com/paper/2411.10842/) (2024)
- [Grading Conversational Responses Of Chatbots](https://mlsanity.com/paper/2303.12038/) (2023)

## Abstract (arXiv)

The research applies AI-driven code assistants to analyze a selection of influential computer code that has shaped modern technology, including email, internet browsing, robotics, and malicious software. The original contribution of this study was to examine half of the most significant code advances in the last 50 years and, in some cases, to provide notable improvements in clarity or performance. The AI-driven code assistant could provide insights into obfuscated code or software lacking explanatory commentary in all cases examined. We generated additional sample problems based on bug corrections and code optimizations requiring much deeper reasoning than a traditional Google search might provide. Future work focuses on adding automated documentation and code commentary and translating select large code bases into more modern versions with multiple new application programming interfaces (APIs) and chained multi-tasks. The AI-driven code assistant offers a valuable tool for software engineering, particularly in its ability to provide human-level expertise and assist in refactoring legacy code or simplifying the explanation or functionality of high-value repositories.