7 citations · 21 across the 20 of their papers we have counts for
22 papers
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…
Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring
Jamshid Mozafari, Bhawna Piryani, Adam Jatowt
Estimating question difficulty is a critical component in evaluating and improving large language models (LLMs) for question answering (QA). Existing approaches often rely on reada…
Pretraining Exposure Explains Popularity Judgments in Large Language Models
Jamshid Mozafari, Bhawna Piryani, Adam Jatowt
Large language models (LLMs) exhibit systematic preferences for well-known entities, a phenomenon often attributed to popularity bias. However, the extent to which these preference…
Context Convergence Improves Answering Inferential Questions
Jamshid Mozafari, Bhawna Piryani, Adam Jatowt
While Large Language Models (LLMs) are widely used in open-domain Question Answering (QA), their ability to handle inferential questions-where answers must be derived rather than d…
BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination
Abdelrahman Abdallah, Mohammed Ali, Bhawna Piryani +1
Reasoning-intensive retrieval requires deep semantic inference beyond surface-level keyword matching, posing a challenge for current LLM-based rerankers limited by context constrai…
How often do Answers Change? Estimating Recency Requirements in Question Answering
Bhawna Piryani, Zehra Mert, Adam Jatowt
Large language models (LLMs) often rely on outdated knowledge when answering time-sensitive questions, leading to confident yet incorrect responses. Without explicit signals indica…
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…
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…
Evaluating Answer Reranking Strategies in Time-sensitive Question Answering
Mehmet Kardan, Bhawna Piryani, Adam Jatowt
Despite advancements in state-of-the-art models and information retrieval techniques, current systems still struggle to handle temporal information and to correctly answer detailed…
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…
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…
DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification
Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani +2
This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditio…
Detecting Temporal Ambiguity in Questions
Bhawna Piryani, Abdelrahman Abdallah, Jamshid Mozafari +1
Detecting and answering ambiguous questions has been a challenging task in open-domain question answering. Ambiguous questions have different answers depending on their interpretat…
Exploring Hint Generation Approaches in Open-Domain Question Answering
Jamshid Mozafari, Abdelrahman Abdallah, Bhawna Piryani +1
Automatic Question Answering (QA) systems rely on contextual information to provide accurate answers. Commonly, contexts are prepared through either retrieval-based or generation-b…
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages
Bhawna Piryani, Jamshid Mozafari, Adam Jatowt
Question answering (QA) and Machine Reading Comprehension (MRC) tasks have significantly advanced in recent years due to the rapid development of deep learning techniques and, more…