SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

Showing 38513900 of 17610 papers

TitleStatusHype
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
TrajAgent: An Agent Framework for Unified Trajectory ModellingCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
TRAMS: Training-free Memory Selection for Long-range Language ModelingCode1
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray imagesCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Transformer Fusion with Optimal TransportCode1
Transformer on a DietCode1
Transformers are RNNs: Fast Autoregressive Transformers with Linear AttentionCode1
Effective Sequence-to-Sequence Dialogue State TrackingCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event ExtractionCode1
Effective Batching for Recurrent Neural Network GrammarsCode1
Effective Attention Sheds Light On InterpretabilityCode1
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
CTRL: A Conditional Transformer Language Model for Controllable GenerationCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Efficient Nearest Neighbor Language ModelsCode1
Benchmarking Language Model Creativity: A Case Study on Code GenerationCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model EvaluationCode1
Advancing High Resolution Vision-Language Models in BiomedicineCode1
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNetCode1
T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question AnsweringCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
TurkishBERTweet: Fast and Reliable Large Language Model for Social Media AnalysisCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
TweepFake: about Detecting Deepfake TweetsCode1
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet ClassificationCode1
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language TechnologiesCode1
Tying Word Vectors and Word Classifiers: A Loss Framework for Language ModelingCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
AVocaDo: Strategy for Adapting Vocabulary to Downstream DomainCode1
UDApter -- Efficient Domain Adaptation Using AdaptersCode1
DynaPipe: Optimizing Multi-task Training through Dynamic PipelinesCode1
ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented LearningCode1
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test ConstructionCode1
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric ApproachCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
dMel: Speech Tokenization made SimpleCode1
Uncertainty-Aware Evaluation for Vision-Language ModelsCode1
A Framework for Inference Inspired by Human Memory MechanismsCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
DziriBERT: a Pre-trained Language Model for the Algerian DialectCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified