2 citations · 2 across the 10 of their papers we have counts for
12 papers
SmallReason-ColBERT: An Ultra-Small Late-Interaction Retriever for Reasoning Intensive Retrieval
Abdelrahman Abdallah, Mohammed Ali, Adam Jatowt
Reasoning-intensive retrieval remains difficult for small models. Compact public ColBERTs are usually trained on general-purpose corpora and underperform reasoning-tuned 150M+ base…
Large Language Models Systematically Favor Popular Options: Evidence and Mitigation Across MCQs
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani +2
Multiple-choice questions (MCQs) are a standard format for evaluating large language models (LLMs), yet the popularity of answer options can confound evaluation. Modern LLMs system…
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…
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,…
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…
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…
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…
RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedback
Abdelrahman Abdallah, Mahmoud Abdalla, Bhawna Piryani +3
Evaluating the quality of retrieval-augmented generation (RAG) and document reranking systems remains challenging due to the lack of scalable, user-centric, and multi-perspective e…
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…
From Retrieval to Generation: Comparing Different Approaches
Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani +2
Knowledge-intensive tasks, particularly open-domain question answering (ODQA), document reranking, and retrieval-augmented language modeling, require a balance between retrieval ac…
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…
Towards Detecting, Recognizing, and Parsing the Address Information from Bangla Signboard: A Deep Learning-based Approach
Hasan Murad, Mohammed Eunus Ali
Retrieving textual information from natural scene images is an active research area in the field of computer vision with numerous practical applications. Detecting text regions and…