SOTAVerified

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 501–550 of 1978 papers

TitleStatusHype
LLM-assisted Vector Similarity Search—0
Extracting triples from dialogues for conversational social agents—0
Research on Violent Text Detection System Based on BERT-fasttext Model—0
Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque—0
Meta-Reflection: A Feedback-Free Reflection Learning Framework—0
Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data—0
NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models—0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI—0
When Text Embedding Meets Large Language Model: A Comprehensive Survey—0
Structural Entropy Guided Probabilistic CodingCode0
Continual Learning for Encoder-only Language Models via a Discrete Key-Value Bottleneck—0
PAFFA: Premeditated Actions For Fast Agents—0
Rethinking Emotion Annotations in the Era of Large Language Models—0
ChocoLlama: Lessons Learned From Teaching Llamas Dutch—0
TrojanWhisper: Evaluating Pre-trained LLMs to Detect and Localize Hardware Trojans—0
A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges—0
Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey—0
Text Is Not All You Need: Multimodal Prompting Helps LLMs Understand Humor—0
Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIPCode0
Consolidating and Developing Benchmarking Datasets for the Nepali Natural Language Understanding Tasks—0
Zero-shot Slot Filling in the Age of LLMs for Dialogue Systems—0
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey—0
Dynamic Self-Distillation via Previous Mini-batches for Fine-tuning Small Language Models—0
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting—0
Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks—0
Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering—0
Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models—0
Neurosymbolic Graph Enrichment for Grounded World Models—0
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation—0
Hysteresis Activation Function for Efficient InferenceCode0
Med-Bot: An AI-Powered Assistant to Provide Accurate and Reliable Medical Information—0
Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understanding—0
Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models—0
Evaluating Large Language Models on Financial Report Summarization: An Empirical Study—0
HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals—0
Improved intent classification based on context information using a windows-based approach—0
GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching—0
Sentiment Analysis of Cyberbullying Data in Social MediaCode0
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank AdaptationCode0
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning—0
AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution—0
Ontology Population using LLMs—0
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior—0
Improving In-Context Learning with Small Language Model EnsemblesCode0
SVIP: Towards Verifiable Inference of Open-source Large Language Models—0
Improving Multimodal Large Language Models Using Continual Learning—0
Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data—0
Improving Small-Scale Large Language Models Function Calling for Reasoning Tasks—0
BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning—0
Task Calibration: Calibrating Large Language Models on Inference Tasks—0
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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