First Large Language Models for Model-Driven Engineering Workshop (LLM4MDE 2024)LLM4MDE 2024

First Workshop on Large Language Models For Model Driven Engineering (LLM4MDE 2024)

The introduction of ChatGPT, Google’s Bard, and Bing Chat Ai marked a significant advancement in the field of natural language processing, contributing to the widespread popularity of large language models (LLMs). These models have found successful applications in diverse sectors, including healthcare, finance, education, and various engineering fields, among others.

LLMs tailored for code, such as CodeLLama, Codex, and others, have revolutionized the landscape of software engineering. They significantly enhance developers’ efficiency in coding, testing, and documentation. Despite their achievements in automating coding tasks, LLMs have not yet made a substantial impact in the domain of Model-Driven Engineering (MDE).

Hence, the primary objective of this workshop is to explore potential applications where LLMs can support MDE engineers and engage in discussions regarding the implementation of such solutions.

The objectives of the llm4mde 2024 workshop are to:

• identify use-cases within MDE that can be assisted by LLMs;
• explore the current technologies that enable the tuning of LLMs to optimize MDE processes;
• identify LLMs that could be applied in the MDE field;
• explore MDE technologies for automating the tuning and configuration of LLMs.

Accepted Papers

Call for Papers

The objectives of the LLM4MDE 2024 workshop are to:

• Identify use-cases within MDE that can be assisted by LLMs;
• Explore the current technologies that enable the tuning of LLMs to optimize MDE processes;
• Identify LLMs that could be applied in the MDE field;
• Explore MDE technologies for automating the tuning and configuration of LLMs.

Topic of Interest

Topics of interest for the workshop include, but are not limited to:

• LLM-augmented modelling tools.
• LLMs to support MDE tasks.
• Prompt engineering techniques to adapt LLMs in the context of MDE.
• Retrieval Augmentation Generation (RAG) to support MDE.
• Performance evaluation of LLMs in a MDE context.
• Benchmarks to assess the modeling capabilities of LLMs.
• LLMs trained with modelling corpora.
• LLMs for supporting low-code development.

Submission Info

Two kinds of papers are solicited:

• regular papers (10 pages + 1 page for references)
• short papers (4 pages + 1 page for references)

Contributions should present novel research ideas (even if at a preliminary development stage), challenging problems, and practical contributions to the domain. Industrial experience reports or case studies related to the development or use of LLMs in an MDE context are also solicited. Furthermore, extended abstract are accepted, that can include prior work or visionary work, but will not included in the proceedings. All papers must be written in English adhere to the CEUR Style single-column formatting guidelines, and be submitted through EasyChair at the link: https://easychair.org/my/conference?conf=staf2024 and then selecting the track Large Language Models for Model-Driven Engineering .

Important Dates

• Abstract Submission: May 13th, 2024 AoE
• Paper Submission: May 13th, 2024 AoE
• Author Notification: June 11th, 2024 AoE
• Camera-Ready: June 18th, 2024 AoE