collaborators

9 papers

cs.IR2026

EXCISE: Query-Side Exclusion for Late-Interaction Retrieval

Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt

Late-interaction retrievers handle exclusion queries poorly. When a user asks for X but not Z, the additive MaxSim score promotes documents covering Z, a problem we call exclusion…

cs.IR2026

MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval

Abdelrahman Abdallah, Mohamed Darwish Mounis, Mahmoud Abdalla +6

Existing retrieval benchmarks primarily consist of text-based queries where keyword or semantic matching is usually sufficient. Many real-world queries contain multimodal elements,…

cs.IR2026

TEMPO: A Realistic Multi-Domain Benchmark for Temporal Reasoning-Intensive Retrieval

Abdelrahman Abdallah, Mohammed Ali, Muhammad Abdul-Mageed +1

Existing temporal QA benchmarks focus on simple fact-seeking queries from news corpora, while reasoning-intensive retrieval benchmarks lack temporal grounding. However, real-world…

cs.IR2026

RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark

Mohammed Ali, Abdelrahman Abdallah, Amit Agarwal +2

Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. To bridge this gap, we present a b…

cs.IR2025

SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting

Mohammed Ali, Abdelrahman Abdallah, Adam Jatowt

The growing demand for corporate sustainability transparency, particularly under new regulations like the EU Taxonomy, necessitates precise data extraction from large, unstructured…

cs.CL2025

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models

Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari +2

In this work, we present a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods, encompassing large language model (LLM)-based, lightweight conte…