Papers by Ines Arous
Rethinking Full Finetuning from Pretraining Checkpoints in Active Learning for African Languages (2025.acl-srw)
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| Challenge: | Existing approaches to improve model performance are finetuning on all acquired data after each round, which is computationally expensive in multilingual and low-resource settings. |
| Approach: | They evaluate continual finetuning (CF) against full finetuned (FA) across 28 African languages using MasakhaNEWS and SIB-200. |
| Outcome: | The proposed approach outperforms full finetuning (FA) in 28 African languages, achieving up to 35% reductions in GPU memory, FLOPs, and training time. |
GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews (2024.acl-long)
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| Challenge: | Scientific peer review is essential for the quality of academic publications. |
| Approach: | They propose a method that summarises scholarly reviews using a Rational Speech Act framework and novel uniqueness scores. |
| Outcome: | The proposed method generates more discriminative summaries than baseline methods in terms of human evaluation while achieving comparable performance with these methods in term of automatic metrics. |
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness (2023.findings-emnlp)
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| Challenge: | Existing work on editing LLMs neglects the dependency between a fact and its logical implications. |
| Approach: | They propose an evaluation protocol that assesses the editing process using StandUp . they aim to ensure that the editing respects internal logical constraints . |
| Outcome: | The proposed evaluation protocol assesses the editing process using a standup question-answering dataset. |
Active Learning with Non-Uniform Costs for African Natural Language Processing (2026.findings-eacl)
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| Challenge: | Annotating datasets for African languages is challenging due to the continent's vast linguistic diversity, complicating development of NLP systems. |
| Approach: | They propose a cost-aware active learning method that integrates BatchBALD acquisition strategy with a 0-1 Knapsack optimization objective to select informative and budget-efficient samples. |
| Outcome: | The proposed method outperforms BALD, BatchBALD, and stochastic sampling variants across cost scenarios on the MasakhaNEWS multilingual news classification benchmark covering 11 African languages. |