3 citations · 7 across the 27 of their papers we have counts for
12 papers · 1 filter
DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation
Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani +1
Large Language Models (LLMs) have transformed listwise document reranking by enabling global reasoning over candidate sets, yet single models often struggle to balance fine-grained…
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…
It's High Time: A Survey of Temporal Question Answering
Bhawna Piryani, Abdelrahman Abdallah, Jamshid Mozafari +2
Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Temporal Question Answering (TQA), a…
A Study into Investigating Temporal Robustness of LLMs
Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt +1
Large Language Models (LLMs) encapsulate a surprising amount of factual world knowledge. However, their performance on temporal questions and historical knowledge is limited becaus…
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…
TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions
Abdelrahman Abdallah, Bhawna Piryani, Jonas Wallat +2
Temporal awareness is crucial in many information retrieval tasks, particularly in scenarios where the relevance of documents depends on their alignment with the query's temporal c…
Evaluating Robustness of LLMs in Question Answering on Multilingual Noisy OCR Data
Bhawna Piryani, Jamshid Mozafari, Abdelrahman Abdallah +2
Optical Character Recognition (OCR) plays a crucial role in digitizing historical and multilingual documents, yet OCR errors - imperfect extraction of text, including character ins…
Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores
Jamshid Mozafari, Abdelrahman Abdallah, Bhawna Piryani +1
Large Language Models (LLMs) are revolutionizing information retrieval, with chatbots becoming an important source for answering user queries. As by their design, LLMs prioritize g…
HintEval: An Open-Source Python Toolkit for Hint Generation and Hint Evaluation
Jamshid Mozafari, Bhawna Piryani, Abdelrahman Abdallah +1
Large Language Models (LLMs) increasingly provide direct answers to user questions, raising concerns about reduced engagement in critical thinking and problem-solving. Hint generat…