# Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming

arXiv: https://arxiv.org/abs/2312.13905 · HTML: https://mlsanity.com/paper/2312.13905/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Large Language Models as Zero-Shot Human Models for Human-Robot Interaction](https://mlsanity.com/paper/2303.03548/) (2023)

## Built on by (most cited first, 1 of 1)

- [Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks](https://mlsanity.com/paper/2405.02357/) (2024)

## Abstract (arXiv)

Industrial robots are applied in a widening range of industries, but robot programming mostly remains a task limited to programming experts. We propose a natural language-based assistant for programming of advanced, industrial robotic applications and investigate strategies for domain-specific fine-tuning of foundation models with limited data and compute.