# Service Composition in the ChatGPT Era

arXiv: https://arxiv.org/abs/2305.15788 · HTML: https://mlsanity.com/paper/2305.15788/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 3 of 3)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)

## Built on by (most cited first, 4 of 4)

- [DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2404.03275/) (2024)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Initial Steps in Integrating Large Reasoning and Action Models for Service Composition](https://mlsanity.com/paper/2507.18775/) (2025)
- [Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking](https://mlsanity.com/paper/2604.22760/) (2026)

## Abstract (arXiv)

The paper speculates about how ChatGPT-like systems can support the field of automated service composition and identifies new research areas to explore in order to take advantage of such tools in the field of service-oriented composition.