7 citations · 31 across the 21 of their papers we have counts for
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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…
PARSE: An Open-Domain Reasoning Question Answering Benchmark for Persian
Jamshid Mozafari, Seyed Parsa Mousavinasab, Adam Jatowt
Reasoning-focused Question Answering (QA) has advanced rapidly with Large Language Models (LLMs), yet high-quality benchmarks for low-resource languages remain scarce. Persian, spo…
Inferential Question Answering
Jamshid Mozafari, Hamed Zamani, Guido Zuccon +1
Despite extensive research on a wide range of question answering (QA) systems, most existing work focuses on answer containment-i.e., assuming that answers can be directly extracte…
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…
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…
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…
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…
WikiHint: A Human-Annotated Dataset for Hint Ranking and Generation
Jamshid Mozafari, Florian Gerhold, Adam Jatowt
The use of Large Language Models (LLMs) has increased significantly with users frequently asking questions to chatbots. In the time when information is readily accessible, it is cr…
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…
Navigating the Landscape of Hint Generation Research: From the Past to the Future
Anubhav Jangra, Jamshid Mozafari, Adam Jatowt +1
Digital education has gained popularity in the last decade, especially after the COVID-19 pandemic. With the improving capabilities of large language models to reason and communica…
TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
Jamshid Mozafari, Anubhav Jangra, Adam Jatowt
Nowadays, individuals tend to engage in dialogues with Large Language Models, seeking answers to their questions. In times when such answers are readily accessible to anyone, the s…
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…
BAS: An Answer Selection Method Using BERT Language Model
Jamshid Mozafari, Afsaneh Fatemi, Mohammad Ali Nematbakhsh
In recent years, Question Answering systems have become more popular and widely used by users. Despite the increasing popularity of these systems, the their performance is not even…
Attention-based Pairwise Multi-Perspective Convolutional Neural Network for Answer Selection in Question Answering
Jamshid Mozafari, Mohammad Ali Nematbakhsh, Afsaneh Fatemi
Over the past few years, question answering and information retrieval systems have become widely used. These systems attempt to find the answer of the asked questions from raw text…