A large-scale empirical study of six zero-cost Query Performance Prediction metrics makes them a practical zero-cost replacement for magnitude thresholding in deployed RAG systems, making them a practical zero-cost replacement for magnitude thresholding in deployed RAG systems.
J. Holdcroft, Abdelrahman Abdallah, Adam Jatowt· 2 citations
This work proposes query-difficulty-gated fusion of reasoning views, a fused ranking that uses no relevance labels at inference, no re-ranking, and no fine-tuning of the retriever; the gate is trained leave-one-task-out.
J. Holdcroft, Abdelrahman Abdallah, Adam Jatowt· 0 citations
Across six collections and three backbones, EXCISE is the strongest system in all eighteen backbone-collection cells against that backbone's own frozen and fine-tuned baselines, and outperforms every fine-tuned cross-encoder, each of which loses no-harm nDCG@10.
Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt· 0 citations
HintEval, an open-source Python library for unified hint generation and evaluation, facilitates systematic research on hint-based question answering (QA) in NLP and IR through human studies in which participants assess generated hints and use them to answer questions.
Jamshid Mozafari, Bhawna Piryani, Abdelrahman Abdallah et al.· 4 citations
A lightweight inference-time correction that estimates and removes a popularity prior from model predictions is proposed, which requires no fine-tuning, is label-free at test time, and adds negligible computational cost.
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
