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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

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Showing 51–100 of 1978 papers

TitleStatusHype
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsCode2
BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language ModelsCode2
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language VariantsCode2
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence UnderstandingCode2
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language ModelsCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language ModelsCode2
Autonomous GIS: the next-generation AI-powered GISCode2
PMC-LLaMA: Towards Building Open-source Language Models for MedicineCode2
Scaling Transformer to 1M tokens and beyond with RMTCode2
NusaCrowd: Open Source Initiative for Indonesian NLP ResourcesCode2
Solving Quantitative Reasoning Problems with Language ModelsCode2
JGLUE: Japanese General Language Understanding EvaluationCode2
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse LanguagesCode2
PERT: Pre-training BERT with Permuted Language ModelCode2
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingCode2
GPT Understands, TooCode2
Learning Transferable Visual Models From Natural Language SupervisionCode2
Spark NLP: Natural Language Understanding at ScaleCode2
I-BERT: Integer-only BERT QuantizationCode2
It's Not Just Size That Matters: Small Language Models Are Also Few-Shot LearnersCode2
DeBERTa: Decoding-enhanced BERT with Disentangled AttentionCode2
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTCode2
CLUE: A Chinese Language Understanding Evaluation BenchmarkCode2
Using Speech Synthesis to Train End-to-End Spoken Language Understanding ModelsCode2
YAKE! Keyword extraction from single documents using multiple local featuresCode2
Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM CompressionCode1
OET: Optimization-based prompt injection Evaluation ToolkitCode1
DeLoRA: Decoupling Angles and Strength in Low-rank AdaptationCode1
Bridging Text and Vision: A Multi-View Text-Vision Registration Approach for Cross-Modal Place RecognitionCode1
Transformers Boost the Performance of Decision Trees on Tabular Data across Sample SizesCode1
Joint Localization and Activation Editing for Low-Resource Fine-TuningCode1
A Survey of RWKVCode1
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
DroidCall: A Dataset for LLM-powered Android Intent InvocationCode1
MEG: Medical Knowledge-Augmented Large Language Models for Question AnsweringCode1
Beyond Autoregression: Fast LLMs via Self-Distillation Through TimeCode1
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation ModelsCode1
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight CompressionCode1
MTL-LoRA: Low-Rank Adaptation for Multi-Task LearningCode1
TextLap: Customizing Language Models for Text-to-Layout PlanningCode1
Large Language Model Inference Acceleration: A Comprehensive Hardware PerspectiveCode1
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal NuancesCode1
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuningCode1
SuperGLEBer: German Language Understanding Evaluation BenchmarkCode1
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional AdaptationCode1
On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and ReasoningCode1
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and DesignCode1
VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector BanksCode1
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table DecompositionCode1
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All datasetsPDP60STREUSLELexGLUEDialoGLUE fewshotDialoGLUE fullGLUE

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HNNAccuracy90—Unverified
2UDSSM-II (ensemble)Accuracy78.3—Unverified
3BERT-large 340MAccuracy78.3—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