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CEA-List@EvalLLM2024: prompting a large language model or fine-tuning a smaller one?

Published in EvalLLM2024 , 2024

The EvalLLM2024 challenge aims to evaluate the results of few-shot approaches to information extraction in French. Our contribution to this challenge tests two approaches: one exploits the available annotated data in the prompt of an LLM (in context learning) while the other fine-tunes a generic entity recognition model (GLiNER) by exploiting the annotated data. Our experiments show that this second approach obtains the best results, especially when enriched by a data augmentation step exploiting the annotation guide and LLMs for the generation of synthetic examples.

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GLiDRE : Generalist model for Document-level Relation Extraction

Published in EGC - Atelier TextMine, 2025

This work adapts the GLiNER model for document-level relation extraction in French, as part of the TextMine 2025 challenge. Enhancements include pretraining on a subset of OSCAR dataset, local representations inspired by ATLOP, and optimized prediction thresholds. Results demonstrate modest performance but the model demonstrates potential in low-resource scenarios.

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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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