Papers by Abdelrahman Abdallah
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. |
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 . |
RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark (2026.findings-acl)
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| Challenge: | Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. |
| Approach: | They propose a framework that transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation. |
| Outcome: | The proposed framework outperforms existing systems in a number of domains and can be used to improve multi-turn conversation retrieval. |
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. |
BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination (2026.acl-long)
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| Challenge: | Existing lists of document ranking methods lack robust performance across domains. |
| Approach: | They propose a reasoning-driven competitive elimination framework that optimises group sizes based on LLM context limits and reasoning-enhanced prompts. |
| Outcome: | The proposed method outperforms RankGPT and other state-of-the-art methods on datasets with a 77.90 NDCG@5 score and 54.66 average NDGC@10 on BEIR 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. |
ComplexTempQA: A 100m Dataset for Complex Temporal Question Answering (2025.emnlp-main)
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| Challenge: | Existing datasets that focus on temporal knowledge are limited in size and lack comprehensive coverage of temporal information. |
| Approach: | They introduce a large-scale temporal question-answer-matching dataset . the new taxonomy categorizes questions as attributes, comparisons, and counting questions . |
| Outcome: | The proposed dataset surpasses existing benchmarks in scale and scope. |
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. |
A Study into Investigating Temporal Robustness of LLMs (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are limited in their ability to process temporal information and perform tasks requiring temporal reasoning and factual knowledge. |
| Approach: | They propose to use eight time-sensitiverobustness tests to test the model's temporal robustness for user questions in the zero-shot setting. |
| Outcome: | The proposed tests improve the temporal QA performance by up to 55%. |
Negative Sampling Techniques in Dense Retrieval: A Survey (2026.findings-eacl)
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| Challenge: | Information Retrieval (IR) is fundamental to many modern NLP applications. |
| Approach: | They propose a taxonomy that categorizes negative sampling techniques in dense IR . they analyze them with respect to trade-offs between effectiveness, computational cost, implementation difficulty . |
| Outcome: | The proposed taxonomy categorizes techniques using random, static/dynamically mined, and synthetic datasets. |
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. |