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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 39514000 of 17610 papers

TitleStatusHype
Effective Attention Sheds Light On InterpretabilityCode1
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed LanguageCode1
DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNACode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
DARTS: Differentiable Architecture SearchCode1
Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language ModelCode1
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBenchCode1
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge TransferCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Efficient Long Sequence Modeling via State Space Augmented TransformerCode1
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic ParsingCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Beheshti-NER: Persian Named Entity Recognition Using BERTCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
Advancing Beyond Identification: Multi-bit Watermark for Large Language ModelsCode1
VisorGPT: Learning Visual Prior via Generative Pre-TrainingCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
Revisiting the Role of Language Priors in Vision-Language ModelsCode1
DynaPipe: Optimizing Multi-task Training through Dynamic PipelinesCode1
Visually-Augmented Language ModelingCode1
Visually Grounded Commonsense Knowledge AcquisitionCode1
DziriBERT: a Pre-trained Language Model for the Algerian DialectCode1
BECEL: Benchmark for Consistency Evaluation of Language ModelsCode1
An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model TokenizersCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language ModelsCode1
ViLA: Efficient Video-Language Alignment for Video Question AnsweringCode1
Data Efficient Masked Language Modeling for Vision and LanguageCode1
A Cheaper and Better Diffusion Language Model with Soft-Masked NoiseCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Dynamic Grained Encoder for Vision TransformersCode1
VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene CompletionCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal LearningCode1
An Efficient Self-Supervised Cross-View Training For Sentence EmbeddingCode1
DUnE: Dataset for Unified EditingCode1
End-to-End Beam Retrieval for Multi-Hop Question AnsweringCode1
An Efficient Multilingual Language Model Compression through Vocabulary TrimmingCode1
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural NetworksCode1
DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and Streaming CapabilitiesCode1
Do Unlearning Methods Remove Information from Language Model Weights?Code1
Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered ChatbotCode1
Balanced Data Sampling for Language Model Training with ClusteringCode1
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language ModelsCode1
Dual Learning with Dynamic Knowledge Distillation for Partially Relevant Video RetrievalCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
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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