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cs.CL2026
MUCH: A Multilingual Claim Hallucination Benchmark
Jérémie Dentan, Alexi Canesse, Davide Buscaldi +2
Claim-level Uncertainty Quantification (UQ) is a promising approach to mitigate the lack of reliability in Large Language Models (LLMs). We introduce MUCH, the first claim-level UQ…
cs.CL2025
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering
Sofian Chaybouti, Achraf Saghe, Aymen Shabou
This paper introduces MIX, a multi-task deep learning approach to solve open-ended question-answering. First, we design our system as a multi-stage pipeline of 3 building blocks: a…
cs.CL2025
EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System
Sofian Chaybouti, Achraf Saghe, Aymen Shabou
State-of-the-art extractive question-answering models achieve superhuman performances on the SQuAD benchmark. Yet, they are unreasonably heavy and need expensive GPU computing to a…