Papers by Shaykhah Alsubaie
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024.acl-long)
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Norah Alzahrani, Hisham Alyahya, Yazeed Alnumay, Sultan AlRashed, Shaykhah Alsubaie, Yousef Almushayqih, Faisal Mirza, Nouf Alotaibi, Nora Al-Twairesh, Areeb Alowisheq, M Saiful Bari, Haidar Khan
| Challenge: | Existing leaderboards are often taken at face value, but this is costly . a recent study shows that minor perturbations to the benchmark result in rankings up to 8 positions. |
| Approach: | They propose to use a *hybrid* scoring method for answer selection for large language models . they find that minor perturbations to the benchmark result in rankings changes . |
| Outcome: | The proposed model is a hybrid scoring method, the authors argue . the proposed model could be used to improve the performance of large language models . |