Papers by Sondos Mahmoud Bsharat
Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation (2026.findings-acl)
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| Challenge: | Large language models exhibit strong reasoning when guided by chain-of-thought exemplars . collecting large, high-quality reasoning datasets remains laborious and resource-intensive . |
| Approach: | They propose a prompt-space data augmentation framework for enhancing LLM reasoning . they use a pool of 90 randomly selected reasoning instances to elicit diverse reasoning trajectories . |
| Outcome: | The proposed framework improves accuracy over small-data benchmarks and generalization on out-of-domain reasoning evaluations. |
DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation (2025.acl-long)
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Jennifer Chen, Aidar Myrzakhan, Yaxin Luo, Hassaan Muhammad Khan, Sondos Mahmoud Bsharat, Zhiqiang Shen
| Challenge: | Large-scale RAG systems consume significant computational resources and are prone to generating “hallucinated” content from Humans. |
| Approach: | They propose a framework for distilling RAG knowledge from large-scale language models into small LMs. |
| Outcome: | The proposed method outperforms the prior competitive RAG methods like MiniRAG for SLMs by up to 27.7% using the same models, preserving high-level efficiency and reliability. |