Papers by Jamshid Mozafari
DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation (2025.findings-emnlp)
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| Challenge: | DeAR is an open-source framework that decouples the tasks of LLMs with holistic cross-document analysis. |
| Approach: | They propose an open-source framework that decouples relevance scoring with holistic cross-document analysis. |
| Outcome: | The proposed framework outperforms open-source frameworks in QA and open-domain QA. |
Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring (2026.acl-long)
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| Challenge: | Existing approaches to estimate question difficulty rely on readability formulas, retrieval-based signals, or popularity statistics. |
| Approach: | They propose a method that estimates question difficulty by computing the entropy of plausibility scores over candidate answers. |
| Outcome: | The proposed method outperforms baselines across four QA datasets and shows strong robustness across hyperparameter variations and question types. |
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models (2025.findings-emnlp)
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| Challenge: | a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods is presented. |
| Approach: | They evaluate 22 reranking methods including 40 variants across established benchmarks . primary goal is to determine whether performance disparity exists between LLM-based reranters and lightweight counterparts based on novel queries . |
| Outcome: | The proposed methods perform better on familiar queries than lightweight models, the authors show . |
Exploring Hint Generation Approaches for Open-Domain Question Answering (2024.findings-emnlp)
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| Challenge: | Existing automatic question answering systems rely on contextual information to provide accurate answers. |
| Approach: | They propose a context preparation approach that uses Automatic Hint Generation techniques to generate hints instead of retrieved contexts. |
| Outcome: | The proposed approach surpasses retrieval-based and generation-based methods on three QA datasets. |
ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval (2025.findings-naacl)
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| Challenge: | Retrieval-Augmented Generation models fail to rank the most relevant documents at the top . conventional retrieval methods fail to find the most important documents . |
| Approach: | They propose a new method for scoring retrieved documents using zero-shot answer scent based on a pre-trained large language model to compute the likelihood of document-derived answers aligning with the answer scent. |
| Outcome: | The proposed method improves top-1 retrieval accuracy on NQ, TriviaQA, WebQA, ArchivalQA, HotpotQA, and Entity Questions. |
It’s High Time: A Survey of Temporal Question Answering (2026.acl-long)
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| Challenge: | Temporal Question Answering (TQA) is a research area that focuses on answering questions involving temporal constraints or context. |
| Approach: | They present a comprehensive overview of Temporal Question Answering (TQA) this research area focuses on answering questions involving temporal constraints or context . |
| Outcome: | The proposed frameworks are compared against a range of datasets, tasks, and approaches. |
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation (2026.acl-demo)
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Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari, Andreas Herzinger, Jamie Holdcroft, Adam Jatowt
| Challenge: | Rankify unifies retrieval-augmented generation (RAG) and retrieval based question answering systems. |
| Approach: | They propose an open-source Python toolkit that unifies retrieval-augmented generation in a single modular framework. |
| Outcome: | The proposed framework unifies retrieval-augmented generation (RAG) tools in a single modular framework. |
DynRank: Improve Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification (2025.coling-main)
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Abdelrahman Elsayed Mahmoud Abdallah, Jamshid Mozafari, Bhawna Piryani, Mohammed M.Abdelgwad, Adam Jatowt
| Challenge: | Existing approaches to enhancing passage retrieval rely on static prompts and pre-defined templates. |
| Approach: | They propose a dynamic question classification framework for open-domain question-answering systems that generates contextually relevant prompts. |
| Outcome: | The proposed framework improves passage retrieval in open-domain questionanswering systems by generating contextually relevant prompts. |
Detecting Temporal Ambiguity in Questions (2024.findings-emnlp)
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| Challenge: | Ambiguous questions have different answers depending on their interpretation and can take diverse forms. |
| Approach: | They propose a manually annotated temporally ambiguous QA dataset that captures temporal ambiguity and propose different search strategies based on disambiguate versions of the questions. |
| Outcome: | The proposed approach captures temporal ambiguity and provides non-search, competitive baselines for detecting temporal and few-shot ambiguities. |