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 401–450 of 1760 papers

TitleStatusHype
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training—0
Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents—0
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
CuentosIE: can a chatbot about "tales with a message" help to teach emotional intelligence?—0
Towards a Psychology of Machines: Large Language Models Predict Human Memory—0
Video Relationship Detection Using Mixture of ExpertsCode0
SaulLM-7B: A pioneering Large Language Model for Law—0
AceMap: Knowledge Discovery through Academic Graph—0
Predicting Learning Performance with Large Language Models: A Study in Adult Literacy—0
Choose Your Own Adventure: Interactive E-Books to Improve Word Knowledge and Comprehension Skills—0
Do Large Language Models Mirror Cognitive Language Processing?—0
Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inferenceCode0
QASE Enhanced PLMs: Improved Control in Text Generation for MRC—0
CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulness—0
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts—0
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification—0
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading ComprehensionCode0
MULTI: Multimodal Understanding Leaderboard with Text and Images—0
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks—0
An Information-Theoretic Approach to Analyze NLP Classification TasksCode0
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?Code0
Paramanu: A Family of Novel Efficient Generative Foundation Language Models for Indian Languages—0
Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting—0
Persona-centric Metamorphic Relation guided Robustness Evaluation for Multi-turn Dialogue Modelling—0
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition—0
Power in Numbers: Robust reading comprehension by finetuning with four adversarial sentences per example—0
Large Language Models are Null-Shot Learners—0
Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models—0
Developing ChatGPT for Biology and Medicine: A Complete Review of Biomedical Question Answering—0
Towards Efficient Methods in Medical Question Answering using Knowledge Graph EmbeddingsCode0
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey—0
Improving Domain Adaptation through Extended-Text Reading Comprehension—0
Structsum Generation for Faster Text Comprehension—0
Attendre: Wait To Attend By Retrieval With Evicted Queries in Memory-Based Transformers for Long Context Processing—0
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training—0
Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading ComprehensionCode0
Probing Pretrained Language Models with Hierarchy Properties—0
High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models—0
Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated RationalesCode0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need—0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification—0
Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension—0
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia—0
Towards Robust Text Retrieval with Progressive LearningCode0
Orca 2: Teaching Small Language Models How to Reason—0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—0
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals—0
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User PerceptionCode0
Thread of Thought Unraveling Chaotic Contexts—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