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Natural Language Understanding

Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning.

Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 1978 papers

TitleStatusHype
Semantic Change Characterization with LLMs using Rhetorics—0
SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy—0
GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics—0
CCoE: A Compact LLM with Collaboration of Experts—0
Automatic Pruning of Fine-tuning Datasets for Transformer-based Language ModelsCode0
Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024—0
ROSA: Random Subspace Adaptation for Efficient Fine-TuningCode0
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs—0
Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models—0
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuningCode1
Tree Search for Language Model AgentsCode3
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study—0
MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math DataCode2
ViANLI: Adversarial Natural Language Inference for Vietnamese—0
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge AggregationCode5
Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language ModelsCode0
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement LearningCode0
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language UnderstandingCode0
SuperGLEBer: German Language Understanding Evaluation BenchmarkCode1
Encoder vs Decoder: Comparative Analysis of Encoder and Decoder Language Models on Multilingual NLU TasksCode2
LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modeling—0
Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages—0
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional AdaptationCode1
Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language UnderstandingCode0
InternalInspector I^2: Robust Confidence Estimation in LLMs through Internal States—0
LiLiuM: eBay's Large Language Models for e-commerce—0
On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and ReasoningCode1
CodeGemma: Open Code Models Based on Gemma—0
DataComp-LM: In search of the next generation of training sets for language modelsCode7
Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment—0
Large Language Models for Automatic Milestone Detection in Group Discussions—0
MMLU-SR: A Benchmark for Stress-Testing Reasoning Capability of Large Language Models—0
3D-RPE: Enhancing Long-Context Modeling Through 3D Rotary Position Encoding—0
Self-Knowledge Distillation for Learning Ambiguity—0
Transformers meet Neural Algorithmic Reasoners—0
Language Models are Crossword Solvers—0
Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL—0
Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests—0
Paraphrasing in Affirmative Terms Improves Negation Understanding—0
MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension—0
Multi-Prompting Decoder Helps Better Language Understanding—0
GameBench: Evaluating Strategic Reasoning Abilities of LLM AgentsCode3
TLEX: An Efficient Method for Extracting Exact Timelines from TimeML Temporal Graphs—0
Mixture-of-Agents Enhances Large Language Model CapabilitiesCode7
Are Large Language Models the New Interface for Data Pipelines?—0
mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans—0
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract DescriptionsCode0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure ParsingCode0
Role-playing Prompt Framework: Generation and Evaluation—0
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All datasetsPDP60STREUSLELexGLUEDialoGLUE fewshotDialoGLUE fullGLUE

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HNNAccuracy90—Unverified
2BERT-large 340MAccuracy78.3—Unverified
3UDSSM-II (ensemble)Accuracy78.3—Unverified
4UDSSM-I (ensemble)Accuracy76.7—Unverified
5DSSMAccuracy75—Unverified
6UDSSM-IIAccuracy75—Unverified
7BERT-base 110M + MASAccuracy68.3—Unverified
8USSM + Supervised Deepnet + 3 Knowledge BasesAccuracy66.7—Unverified
9Word-level CNN+LSTM (full scoring)Accuracy60—Unverified
10Subword-level Transformer LMAccuracy58.3—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT (pred POS/lemmas)Tags (Full) Acc82.5—Unverified
2BERT (none)Tags (Full) Acc82—Unverified
3BERT (gold POS/lemmas)Tags (Full) Acc81—Unverified
4GloVe (gold POS/lemmas)Tags (Full) Acc79.3—Unverified
5RoBERTa + LinearFull F1 (Preps)78.2—Unverified
6GloVe (none)Tags (Full) Acc77.5—Unverified
7GloVe (pred POS/lemmas)Tags (Full) Acc77.1—Unverified
8SVM (feature-rich, gold syntax)Role F1 (Preps)62.2—Unverified
9BiLSTM + MLP (gold syntax)Role F1 (Preps)62.2—Unverified
10SVM (feature-rich, auto syntax)Role F1 (Preps)58.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CaseLaw-BERTCaseHOLD75.6—Unverified
2Legal-BERTCaseHOLD75.1—Unverified
3DeBERTaCaseHOLD72.1—Unverified
4LongformerCaseHOLD72—Unverified
5RoBERTaCaseHOLD71.7—Unverified
6BERTCaseHOLD70.7—Unverified
7BigBirdCaseHOLD70.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConvBERT-DGAverage74.6—Unverified
2ConvBERT-DG + Pre + MultiAverage73.8—Unverified
3mslmAverage73.49—Unverified
4ConvBERT + Pre + MultiAverage68.22—Unverified
5BanLanGenAverage39.16—Unverified
#ModelMetricClaimedVerifiedStatus
1ConvBERT + Pre + MultiAverage86.89—Unverified
2mslmAverage85.83—Unverified
3ConvBERT-DG + Pre + MultiAverage85.34—Unverified
#ModelMetricClaimedVerifiedStatus
1MT-DNN-SMARTAverage89.9—Unverified
2BERT-LARGEAverage82.1—Unverified