Author

Saleh Almohaimeed

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Open access Aug 2026

Benchmarking retrieval augmented generation LLMs for Arabic noise robustness

This paper systematically investigated the capabilities of state-of-the-art multilingual LLMs with regard to two essential RAG abilities, noise robustness and negative rejection, and revealed that all six LLMs were negatively affected when the noise ratio in the external documents was increased.

Saleh Almohaimeed, Abdulrahman Alabduljabbar, Mousa Jari et al. · Frontiers in Big Data · 1 citation

Preprint Aug 2026

Privacy-Preserving RAG by Concealing Sensitive Information from External LLMs

This paper introduces the Sensitive Entity Alias Generator (SEAG), a privacy-preserving framework that empowers users to utilize powerful third-party generators without disclosing sensitive information and demonstrates the success of the SEAG framework.

Saleh Almohaimeed, Saad Almohaimeed, Mousa Jari et al. · 0 citations