Papers Machine Reading Comprehension
“Machine Reading Comprehension” 태그가 달린 논문 562편 · 필터 해제
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 KnowledgeHeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark
Current benchmarks for Hebrew Natural Language Processing (NLP) focus mainly on morpho-syntactic tasks, neglecting the semantic dimension of language understanding. To bridge this gap, we set out to deliver a Hebrew Mach…
Natural Language UnderstandingMachine Reading ComprehensionQuestion AnsweringInterpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation
Question Answering (QA) poses a challenging and critical problem, particularly in today's age of interactive dialogue systems such as ChatGPT, Perplexity, Microsoft Copilot, etc. where users demand both accuracy and tran…
Information RetrievalKnowledge DistillationMachine Reading ComprehensionProblem Decomposition+2Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From
The ability of cross-lingual context retrieval is a fundamental aspect of cross-lingual alignment of large language models (LLMs), where the model extracts context information in one language based on requests in another…
Machine Reading ComprehensionReading ComprehensionRetrievalInvestigating Recent Large Language Models for Vietnamese Machine Reading Comprehension
Large Language Models (LLMs) have shown remarkable proficiency in Machine Reading Comprehension (MRC) tasks; however, their effectiveness for low-resource languages like Vietnamese remains largely unexplored. In this pap…
Machine Reading ComprehensionReading ComprehensionVietnamese Machine Reading ComprehensionMRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark
Machine Reading Comprehension (MRC) is an essential task in evaluating natural language understanding. Existing MRC datasets primarily assess specific aspects of reading comprehension (RC), lacking a comprehensive MRC be…
Machine Reading ComprehensionNatural Language UnderstandingReading ComprehensionPay Attention to Real World Perturbations! Natural Robustness Evaluation in Machine Reading Comprehension
As neural language models achieve human-comparable performance on Machine Reading Comprehension (MRC) and see widespread adoption, ensuring their robustness in real-world scenarios has become increasingly important. Curr…
Machine Reading ComprehensionReading ComprehensionRoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
Role-playing is important for Large Language Models (LLMs) to follow diverse instructions while maintaining role identity and the role's pre-defined ability limits. Existing role-playing datasets mostly contribute to con…
Instruction FollowingMachine Reading ComprehensionReading ComprehensionVisualizing attention zones in machine reading comprehension models
The attention mechanism plays an important role in the machine reading comprehension (MRC) model. Here, we describe a pipeline for building an MRC model with a pretrained language model and visualizing the effect of each…
Language ModelingLanguage ModellingMachine Reading ComprehensionReading ComprehensionRoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehension
Objective: Scientific publications play a crucial role in uncovering insights, testing novel drugs, and shaping healthcare policies. Accessing the quality of publications requires evaluating their Risk of Bias (RoB), a p…
Binary ClassificationMachine Reading ComprehensionReading ComprehensionIncreasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference
As the cultural heritage sector increasingly adopts technologies like Retrieval-Augmented Generation (RAG) to provide more personalised search experiences and enable conversations with collections data, the demand for sp…
Machine Reading ComprehensionQuestion AnsweringRAGReading Comprehension+2Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models
The development of LLMs has greatly enhanced the intelligence and fluency of question answering, while the emergence of retrieval enhancement has enabled models to better utilize external information. However, the presen…
Contrastive LearningData AugmentationMachine Reading ComprehensionQuestion Answering+1Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension
Performance of NLP systems is typically evaluated by collecting a large-scale dataset by means of crowd-sourcing to train a data-driven model and evaluate it on a held-out portion of the data. This approach has been show…
DiversityLanguage ModelingLanguage ModellingMachine Reading Comprehension+1Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where …
In-Context LearningMachine Reading ComprehensionOpen-Domain Question AnsweringQuestion Answering+2SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models
Large language models (LLMs) have become powerful tools for advancing natural language processing applications in the financial industry. However, existing financial LLMs often face challenges such as hallucinations or s…
ArticlesDomain AdaptationLanguage ModellingLarge Language Model+2