Machine Reading Comprehension
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Benchmarks
Most implemented
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
A Unified MRC Framework for Named Entity Recognition
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Stochastic Answer Networks for Machine Reading Comprehension
Stochastic Answer Networks for SQuAD 2.0
Papers
Automatic Inter-document Multi-hop Scientific QA Generation
Existing automatic scientific question generation studies mainly focus on single-document factoid QA, overlooking the inter-document reasoning crucial for scientific understanding. We present AIM-SciQA, an automated fram…
Machine Reading ComprehensionQuestion GenerationJobResQA: A Benchmark for LLM Machine Reading Comprehension on Multilingual Résumés and JDs
We introduce JobResQA, a multilingual Question Answering benchmark for evaluating Machine Reading Comprehension (MRC) capabilities of LLMs on HR-specific tasks involving résumés and job descriptions. The dataset comprise…
Machine Reading ComprehensionQuestion AnsweringFrom RAG to Agentic RAG for Faithful Islamic Question Answering
Large Language Models (LLMs) are increasingly used for Islamic question answering, where ungrounded responses may carry serious religious consequences. Yet standard MCQ/MRC-style evaluations (MCQ: Multiple choice questio…
Machine Reading ComprehensionQuestion AnsweringEnhancing the QA Model through a Multi-domain Debiasing Framework
Question-answering (QA) models have advanced significantly in machine reading comprehension but often exhibit biases that hinder their performance, particularly with complex queries in adversarial conditions. This study …
Natural Language UnderstandingMachine Reading ComprehensionKnowledge DistillationQuestion AnsweringEnhancing Cross-Lingual Transfer through Reversible Transliteration: A Huffman-Based Approach for Low-Resource Languages
As large language models (LLMs) are trained on increasingly diverse and extensive multilingual corpora, they demonstrate cross-lingual transfer capabilities. However, these capabilities often fail to effectively extend t…
Machine Reading ComprehensionCross-Lingual TransferMachine TranslationText ClassificationDoes This Look Familiar to You? Knowledge Analysis via Model Internal Representations
Recent advances in large language models (LLMs) have been driven by pretraining, supervised fine tuning (SFT), and alignment tuning. Among these, SFT plays a crucial role in transforming a model 's general knowledge into…
Machine Reading ComprehensionPrompt EngineeringGeneral Knowledge