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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1620116250 of 17610 papers

TitleStatusHype
Zero-shot Visual Question Answering with Language Model FeedbackCode0
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-TrainingCode0
Z-Forcing: Training Stochastic Recurrent NetworksCode0
ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language AdaptersCode0
Z-LaVI: Zero-Shot Language Solver Fueled by Visual ImaginationCode0
Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion MiningCode0
ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 editionCode0
Do Pre-trained Vision-Language Models Encode Object States?Code0
CodeBenchGen: Creating Scalable Execution-based Code Generation BenchmarksCode0
Do RNNs learn human-like abstract word order preferences?Code0
Augment or Not? A Comparative Study of Pure and Augmented Large Language Model RecommendersCode0
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic SystemsCode0
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?Code0
Augmenting Self-attention with Persistent MemoryCode0
Augmenting Biomedical Named Entity Recognition with General-domain ResourcesCode0
Do Transformers Need Deep Long-Range MemoryCode0
Auditing Prompt Caching in Language Model APIsCode0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in BlockchainCode0
Identifying Human Needs through Social Media: A study on Indian cities during COVID-19Code0
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?Code0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
Do Vision-Language Models Understand Compound Nouns?Code0
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation ExtractionCode0
Domain-Specific Language Model Pretraining for Biomedical Natural Language ProcessingCode0
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo MethodsCode0
Fennec: Fine-grained Language Model Evaluation and Correction Extended through Branching and BridgingCode0
CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse RelationsCode0
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified TextCode0
Domain-Specific Language Model Post-Training for Indonesian Financial NLPCode0
MonoCoder: Domain-Specific Code Language Model for HPC Codes and TasksCode0
DPPA: Pruning Method for Large Language Model to Model MergingCode0
AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codesCode0
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain AdaptationCode0
A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion DiagnosisCode0
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender BiasCode0
DPTDR: Deep Prompt Tuning for Dense Passage RetrievalCode0
AttViz: Online exploration of self-attention for transparent neural language modelingCode0
Domain Specific Author Attribution Based on Feedforward Neural Network Language ModelsCode0
Domain Private Transformers for Multi-Domain Dialog SystemsCode0
Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context ExpertsCode0
Domain Knowledge Transferring for Pre-trained Language Model via Calibrated Activation Boundary DistillationCode0
Domain-independent Dominance of Adaptive MethodsCode0
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answeringCode0
A Character-Word Compositional Neural Language Model for FinnishCode0
An Effective Domain Adaptive Post-Training Method for BERT in Response SelectionCode0
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model CapabilitiesCode0
Coarse-to-Fine Memory Matching for Joint Retrieval and ClassificationCode0
Attribute Alignment: Controlling Text Generation from Pre-trained Language ModelsCode0
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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