Papers by Ahmed Elhady
WiCkeD: A Simple Method to Make Multiple Choice Benchmarks More Challenging (2025.acl-short)
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| Challenge: | Multiple choice question (MCQ) benchmarks are widely used to evaluate Large Language Models (LLMs). |
| Approach: | They propose a method to increase the complexity of existing multiple-choice benchmarks by randomly replacing a choice with “None of the above”. |
| Outcome: | The proposed method can be applied to 6 popular benchmarks and evaluate 18 open-weight LLMs. |
Improving Factuality in Clinical Abstractive Multi-Document Summarization by Guided Continued Pre-training (2024.naacl-short)
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| Challenge: | Existing methods for enhancing the factual accuracy of abstractive summarization models are not effective in fact-critical domains like clinical document summarizing. |
| Approach: | They propose a guided continued pre-training stage for encoder-decoder models followed by supervised fine-tuning on summarization. |
| Outcome: | The proposed approach improves the quality and factuality of the summaries and achieves the best-published results on the clinical document summarization task. |
Emergent Abilities of Large Language Models under Continued Pre-training for Language Adaptation (2025.acl-long)
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| Challenge: | Existing large language models are notoriously English-centric, and their performance has been reported to drop significantly in lessresourced languages. |
| Approach: | They propose a language-agnostic benchmark for in-context learning that reveals catastrophic forgetting early on CPT when English is not included. |
| Outcome: | The proposed method does not impact validation perplexity but is critical for emergence of downstream capabilities in the target language. |