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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 67516800 of 17610 papers

TitleStatusHype
Syntactic realization with data-driven neural tree grammarsCode0
Scrambled text: training Language Models to correct OCR errors using synthetic dataCode0
On-Device LLM for Context-Aware Wi-Fi RoamingCode0
Syntactic Substitutability as Unsupervised Dependency SyntaxCode0
Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic AmbiguitiesCode0
Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled DataCode0
Machine-generated text detection prevents language model collapseCode0
Laying Anchors: Semantically Priming Numerals in Language ModelingCode0
On-Device Collaborative Language Modeling via a Mixture of Generalists and SpecialistsCode0
Syntax-driven Data Augmentation for Named Entity RecognitionCode0
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant AnnotationsCode0
Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural NetworkCode0
Pun Generation with SurpriseCode0
SCOPE: Sign Language Contextual Processing with Embedding from LLMsCode0
PunKtuator: A Multilingual Punctuation Restoration System for Spoken and Written TextCode0
SCOB: Universal Text Understanding via Character-wise Supervised Contrastive Learning with Online Text Rendering for Bridging Domain GapCode0
PuoBERTa: Training and evaluation of a curated language model for SetswanaCode0
Neuron to Graph: Interpreting Language Model Neurons at ScaleCode0
Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource LanguagesCode0
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical ReasoningCode0
Contextual Knowledge Pursuit for Faithful Visual SynthesisCode0
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection TaskCode0
Synthesizing Interpretable Control Policies through Large Language Model Guided SearchCode0
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data AugmentationCode0
Low-Rank RNN Adaptation for Context-Aware Language ModelingCode0
Exploring the Value of Pre-trained Language Models for Clinical Named Entity RecognitionCode0
Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True DistributionCode0
Synthetic Data Made to Order: The Case of ParsingCode0
Towards Equitable Representation in Text-to-Image Synthesis Models with the Cross-Cultural Understanding Benchmark (CCUB) DatasetCode0
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry GenerationCode0
KitchenScale: Learning to predict ingredient quantities from recipe contextsCode0
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific TopicsCode0
Language Modelling for Source Code with Transformer-XLCode0
Learning to Rank Context for Named Entity Recognition Using a Synthetic DatasetCode0
Pointer-based Fusion of Bilingual Lexicons into Neural Machine TranslationCode0
Systematic word meta-sense extensionCode0
Understanding Stragglers in Large Model Training Using What-if AnalysisCode0
Neural Architecture OptimizationCode0
System-Level Natural Language FeedbackCode0
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled SamplingCode0
Science Out of Its Ivory Tower: Improving Accessibility with Reinforcement LearningCode0
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text ClassificationCode0
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image ManipulationCode0
Boosting Point-BERT by Multi-choice TokensCode0
Scholarly Question Answering using Large Language Models in the NFDI4DataScience GatewayCode0
On Architectures for Including Visual Information in Neural Language Models for Image DescriptionCode0
TabFact: A Large-scale Dataset for Table-based Fact VerificationCode0
Rational RecurrencesCode0
On Anytime Learning at MacroscaleCode0
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuningCode0
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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