Author name cluster

Frédéric Armetta

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3 papers

2 author rows

AAMAS Conference 2025 Conference Paper

Leveraging Graph Structures and Large Language Models for End-to-End Synthetic Task-Oriented Dialogues

Maya Medjad
Hugo Imbert
Bruno Yun
Raphaël Szymocha
Frédéric Armetta

Training task-oriented dialogue systems is both costly and timeconsuming, due to the need for high-quality datasets encompassing diverse intents. Traditional methods depend on extensive human annotation, while recent advancements leverage large language models (LLMs) to generate synthetic data. However, these approaches often require custom prompts or code, limiting accessibility for nontechnical users. We introduce GraphTOD, an end-to-end framework that simplifies the generation of task-oriented dialogues. Users can create dialogues by specifying transition graphs in JSON format. Our evaluation demonstrates that GraphTOD generates highquality dialogues across various domains, significantly lowering the cost and complexity of dataset creation.

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ECAI Conference 2024 Conference Paper

Assisted Debate Builder with Large Language Models

Elliot Faugier
Frédéric Armetta
Angela Bonifati
Bruno Yun

We introduce ADBL2, an assisted debate builder tool. It is based on the capability of large language models to generalise and perform relation-based argument mining in a wide-variety of domains. It is the first open-source tool that leverages relation-based mining for (1) the verification of pre-established relations in a debate and (2) the assisted creation of new arguments by means of large language models. ADBL2 is highly modular and can work with any open-source large language models that are used as plugins. As a by-product, we also provide the first fine-tuned Mistral-7B large language model for relation-based argument mining, usable by ADBL2, which outperforms existing approaches for this task with an overall F1-score of 90. 59% across all domains.

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AAAI Conference 2015 Conference Paper

Multi-Agent Dynamic Coupling for Cooperative Vehicles Modeling

Maxime Guériau
Romain Billot
Nour-Eddin El Faouzi
Salima Hassas
Frédéric Armetta

Cooperative Intelligent Transportation Systems (C-ITS) are complex systems well-suited to a multi-agent modeling. We propose a multi-agent based modeling of a C-ITS, that couples 3 dynamics (physical, informational and control dynamics) in order to ensure a smooth cooperation between non cooperative and cooperative vehicles, that communicate with each other (V2V communication) and the infrastructure (I2V and V2I communication). We present our multi-agent model, tested through simulations using real traffic data and integrated into our extension of the Multi-model Open-source Vehiculartraffic SIMulator (MovSim).

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