Papers by Issam H. Laradji
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches (2025.findings-naacl)
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| Challenge: | Existing approaches for low-resource text summarization use large language models (LLMs) but such models suffer from inconsistent outputs and are difficult to adapt to domain-specific data. |
| Approach: | They propose two methods to effectively utilize large language models for low-resource text summarization. |
| Outcome: | The proposed methods synthesize high-quality documents using LLaMA-3-70b-Instruct model . they achieve competitive ROUGE scores as a fully supervised method with 5% of the labeled data. |
FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering (2025.findings-emnlp)
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| Challenge: | Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications . despite advances in visual question answering, this multihop setting remains underexplored due to a lack of quality datasets. |
| Approach: | They propose a framework for creating a high-quality dataset for multimodal multihop question answering . they use a 5-stage pipeline to acquire relevant multimodal documents from Wikipedia . |
| Outcome: | The proposed framework outperforms existing methods on multimodal multihop question answering datasets. |