Papers by Hakim Hacid
PORT: Preference Optimization on Reasoning Traces (2025.naacl-long)
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| Challenge: | Preference optimization methods have been successfully applied to improve the alignment of large language models with human values. |
| Approach: | They propose to use preference optimization methods to generate rejected answers using weak LLM prompting and digit corruption to improve the mathematical reasoning abilities of language models. |
| Outcome: | The proposed method leads to increased accuracy on the GSM8K and AQuA-RAT benchmarks without annotations. |
Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical Classification (2025.coling-main)
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Shijing Chen, Mohamed Reda Bouadjenek, Usman Naseem, Basem Suleiman, Shoaib Jameel, Flora Salim, Hakim Hacid, Imran Razzak
| Challenge: | Multi-level Hierarchical Classification (MLHC) is a critical tool in modern data analysis. |
| Approach: | They propose a taxonomy-embedded transitional LLM-agnostic framework for multimodality classification that leverages large language models to enforce consistency across hierarchical levels. |
| Outcome: | The proposed framework improves on the MEP-3M dataset with various hierarchical levels compared to conventional models. |