ATLANTIS at SemEval-2025 Task 3: Detecting Hallucinated Text Spans in Question Answering

This paper presents the contributions of the ATLANTIS team to SemEval-2025 Task 3, focusing on detecting hallucinated text spans in question answering systems. Large Language Models (LLMs) have significantly advanced Natural Language Generation (NLG) but remain susceptible to hallucinations, generating incorrect or misleading content. To address this, we explored methods both with and without external context, utilizing few-shot prompting with a LLM, token-level classification or LLM fine-tuned on synthetic data. Notably, our approaches achieved top rankings in Spanish and competitive placements in English and German. This work highlights the importance of integrating relevant context to mitigate hallucinations and demonstrate the potential of fine-tuned models and prompt engineering.

Surrogate Neural Networks Local Stability for Aircraft Predictive Maintenance

Surrogate Neural Networks (NN) now routinely serve as substitutes for computationally demanding simulations (e.g., finite element). They enable faster analyses in industrial applications e.g., manufacturing processes, performance assessment. The verification of surrogate models is a critical step to assess their robustness under different scenarios. We explore the combination of empirical and formal methods in one NN verification pipeline. We showcase its efficiency on an industrial use case of aircraft predictive maintenance. We assess the local stability of surrogate NN designed to predict the stress sustained by an aircraft part from external loads. Our contribution lies in the complete verification of the surrogate models that possess a high-dimensional input and output space, thus accommodating multi-objective constraints. We also demonstrate the pipeline effectiveness in substantially decreasing the runtime needed to assess the targeted property.