SOTAVerified

Reading Comprehension

Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.

Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.

Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.

The Machine Reading group at UCL also provides an overview of reading comprehension tasks.

Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets

Papers

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Showing 301–350 of 1760 papers

TitleStatusHype
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search—0
SciDQA: A Deep Reading Comprehension Dataset over Scientific PapersCode0
Evaluating Large Language Model Capability in Vietnamese Fact-Checking Data Generation—0
A study of Vietnamese readability assessing through semantic and statistical features—0
Diagnosing Medical Datasets with Training DynamicsCode0
NLP and Education: using semantic similarity to evaluate filled gaps in a large-scale Cloze test in the classroom—0
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text—0
LLMs are Biased Evaluators But Not Biased for Retrieval Augmented GenerationCode0
Visualizing attention zones in machine reading comprehension models—0
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsCode0
RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading ComprehensionCode0
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions—0
Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use—0
BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning—0
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models—0
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems—0
Enhanced Electronic Health Records Text Summarization Using Large Language Models—0
On the token distance modeling ability of higher RoPE attention dimension—0
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference—0
Fine-Grained Prediction of Reading Comprehension from Eye MovementsCode0
Punctuation Prediction for Polish Texts using Transformers—0
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations—0
Rehearsing Answers to Probable Questions with Perspective-Taking—0
On the Inductive Bias of Stacking Towards Improving Reasoning—0
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AICode0
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading ComprehensionCode0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?Code0
Evaluating Large Language Models with Tests of Spanish as a Foreign Language: Pass or Fail?—0
DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction—0
Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers—0
FabricQA-Extractor: A Question Answering System to Extract Information from Documents using Natural Language Questions—0
Investigating a Benchmark for Training-set free Evaluation of Linguistic Capabilities in Machine Reading Comprehension—0
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning—0
AutoFAIR : Automatic Data FAIRification via Machine Reading—0
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset ConstructionCode0
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models—0
Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasets—0
SAT3D: Image-driven Semantic Attribute Transfer in 3D—0
LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation—0
How to Engage Your Readers? Generating Guiding Questions to Promote Active ReadingCode0
Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization—0
Struct-X: Enhancing Large Language Models Reasoning with Structured Data—0
Large Language Models as Misleading Assistants in Conversation—0
Look Within, Why LLMs Hallucinate: A Causal Perspective—0
sPhinX: Sample Efficient Multilingual Instruction Fine-Tuning Through N-shot Guided Prompting—0
Have We Reached AGI? Comparing ChatGPT, Claude, and Gemini to Human Literacy and Education Benchmarks—0
AgentInstruct: Toward Generative Teaching with Agentic Flows—0
RVISA: Reasoning and Verification for Implicit Sentiment Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6—Unverified
2AMR-LE-EnsembleTest80—Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3—Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3—Unverified
5Knowledge modelTest79.2—Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2—Unverified
7LReasoner ensembleTest76.1—Unverified
8ELECTRA and ALBERTTest71—Unverified
9WWZTest69.7—Unverified
10xlnet-large-uncased [extended data]Test69.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4—Unverified
2Megatron-BERT (ensemble)Accuracy90.9—Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8—Unverified
4Megatron-BERTAccuracy89.5—Unverified
5XLNetAccuracy (Middle)88.6—Unverified
6DeBERTalargeAccuracy86.8—Unverified
7B10-10-10Accuracy85.7—Unverified
8RoBERTaAccuracy83.2—Unverified
9Orca 2-13BAccuracy82.87—Unverified
10Orca 2-7BAccuracy80.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94—Unverified
2MT5 LargeAverage F10.84—Unverified
3ruRoberta-large finetuneAverage F10.83—Unverified
4ruT5-large-finetuneAverage F10.82—Unverified
5Human BenchmarkAverage F10.81—Unverified
6ruT5-base-finetuneAverage F10.77—Unverified
7ruBert-large finetuneAverage F10.76—Unverified
8ruBert-base finetuneAverage F10.74—Unverified
9RuGPT3XL few-shotAverage F10.74—Unverified
10RuGPT3LargeAverage F10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4—Unverified
2BERT-LargeOverall: F162.7—Unverified
3BiDAFOverall: F128.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55—Unverified