1 citations · 1 across the 3 of their papers we have counts for
4 papers
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari +2
Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical components of modern applications in information retrieval, question answering, or knowledge-based text…
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval
Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani +1
Retrieval-Augmented Generation (RAG) models have drawn considerable attention in modern open-domain question answering. The effectiveness of RAG depends on the quality of the top r…
Navigating Tomorrow: Reliably Assessing Large Language Models Performance on Future Event Prediction
Petraq Nako, Adam Jatowt
Predicting future events is an important activity with applications across multiple fields and domains. For example, the capacity to foresee stock market trends, natural disasters,…
LLMTemporalComparator: A Tool for Analysing Differences in Temporal Adaptations of Large Language Models
Reinhard Friedrich Fritsch, Adam Jatowt
This study addresses the challenges of analyzing temporal discrepancies in large language models (LLMs) trained on data from different time periods. To facilitate the automatic exp…