“Join the CEA to give meaning to your activity, lead and support national and international R&D projects, cultivate and bring to life your spirit of curiosity.” 

IN SUMMARY, WHAT DO WE OFFER YOU?

The CEA is looking for an Engineer in use of LLMs for the specification of data sharing policies for the CEA/DRT (Technological Research Directorate). This 36-month fixed-term management position is based at the Nano-Innov site in Paris-Saclay, Essonne (91).

This is a position to be filled as soon as possible.

WHY JOIN US?

The development of physical or digital systems is complex and involves technical and human challenges. The first step is to formalize ideas by writing specifications or specifications, generally written in natural language by functional analysts. These documents are crucial for the project and facilitate communication between stakeholders. Requirements engineering proposes techniques (reviews, modeling, formalization) to improve the quality of documents (consistency, completeness) and correct defects before the implementation of the system.

Large model neural networks (LLM) bring new possibilities in this field [2]. We propose to use a conversational agent (ChatGPT, Lama) to model data sharing policies (ODRL[1]) from natural text. The tool will recommend rewriting options inspired by INCOSE and EARS standards, analyze the results and provide an audit on the quality of the obtained model.

LLMs are particularly promising for:

• transforming unstructured requirements into structured models such as EARS or user stories [2]
• classifying requirements [3]: behavioral, non-functional, etc. as defined for example in [4]
• report ambiguities, inconsistencies or potential violations based on predefined validation heuristics [5] [2]

However, LLMs have limitations: hallucinations, algorithmic biases and limited generalization [2]. To overcome this, we suggest hybridizing, as in [1], LLMs with other techniques (NLP, process algebras) in order to reduce these impacts.

WHAT DO WE EXPECT FROM YOU?

Within the "Intelligent Requirements" team of the laboratory, your challenge will consist in:

• Determining schemas or a controlled language to represent the ODRL model
• Determining the effectiveness of different techniques and formalisms, such as NLP [6] or inspiration from the Bleu metric [7], to avoid hallucinations during rewriting
• Analyze, manage or generate training data for LLMs
• Configure and manage one or more LLMs with the most effective techniques to improve the consistency and completeness of data sharing policies
• Develop the software tools necessary for the above work.

REQUIRED SKILLS:

Holders of a PhD or Master's degree in computer science, mathematics or systems engineering

DO YOU STILL HAVE A DOUBT?

The side-effects of your main mission may interest us:

• A cutting-edge research ecosystem, unique in its kind and dedicated to themes with high societal stakes, which gives meaning to your mission
• Training to strengthen your skills, acquire new ones and boost your mission
• A work-life balance recognized by our employees
• The possibility of teleworking to balance travel times and contribute to your quality of life
• A WC rich in benefits and social, cultural and sports activities
• A workplace at the heart of a dynamic plateau, surrounded by schools and tech companies

Does this tempt you? Apply, this position is for you!

In accordance with the commitments made by the CEA in favor of the integration of people with disabilities, this job is open to all

#CEA-List #NumericalSimulation #AI

[1]       « ODRL Information Model 2.2 » : https://www.w3.org/TR/odrl-model/
[2]       C. Arora, J. Grundy, et M. Abdelrazek, « Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs », 1 novembre 2023, arXiv: arXiv:2310.13976 : http://arxiv.org/abs/2310.13976
[3]       X. Luo, Y. Xue, Z. Xing, et J. Sun, « PRCBERT: Prompt Learning for Requirement Classification using BERT-based Pretrained Language Models », in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester MI USA: ACM, oct. 2022, p. 1‑13. doi: 10.1145/3551349.3560417.
[4]       A. Fan et al., « Large Language Models for Software Engineering: Survey and Open Problems », 11 novembre 2023, arXiv: arXiv:2310.03533. doi: 10.48550/arXiv.2310.03533.
[5]       D. V. Dzung et A. Ohnishi, « Improvement of Quality of Software Requirements with Requirements Ontology », in 2009 Ninth International Conference on Quality Software, août 2009, p. 284‑289. doi: 10.1109/QSIC.2009.44.
[6]       I. K. Raharjana, D. Siahaan, et C. Fatichah, « User Stories and Natural Language Processing: A Systematic Literature Review », IEEE Access, vol. 9, p. 53811‑53826, 2021, doi: 10.1109/ACCESS.2021.3070606.
[7]       K. Papineni, S. Roukos, T. Ward, et W.-J. Zhu, « Bleu: a Method for Automatic Evaluation of Machine Translation », in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, P. Isabelle, E. Charniak, et D. Lin, Éd., Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, juill. 2002, p. 311‑318. doi: 10.3115/1073083.1073135.