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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1555115600 of 17610 papers

TitleStatusHype
Semantics-aware BERT for Language UnderstandingCode0
Syllable-aware Neural Language Models: A Failure to Beat Character-aware OnesCode0
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge AlignmentCode0
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2Code0
Semantics or spelling? Probing contextual word embeddings with orthographic noiseCode0
Semantic Specialization for Knowledge-based Word Sense DisambiguationCode0
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation LearningCode0
Towards Equitable Representation in Text-to-Image Synthesis Models with the Cross-Cultural Understanding Benchmark (CCUB) DatasetCode0
Meta-Learning the Difference: Preparing Large Language Models for Efficient AdaptationCode0
Syllable Subword Tokens for Open Vocabulary Speech Recognition in MalayalamCode0
SyllabusQA: A Course Logistics Question Answering DatasetCode0
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERTCode0
Symbolic Discovery of Optimization AlgorithmsCode0
Meta Fine-Tuning Neural Language Models for Multi-Domain Text MiningCode0
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
Meta-Context Transformers for Domain-Specific Response GenerationCode0
Semi-Automated Construction of Food Composition Knowledge BaseCode0
PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech RepresentationsCode0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
PACuna: Automated Fine-Tuning of Language Models for Particle AcceleratorsCode0
Learning Natural Language Generation with Truncated Reinforcement LearningCode0
Semiparametric Token-Sequence Co-SupervisionCode0
Linguistic Versus Latent Relations for Modeling Coherent Flow in ParagraphsCode0
Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-TuningCode0
Typos-aware Bottlenecked Pre-Training for Robust Dense RetrievalCode0
Learning Multiplex Representations on Text-Attributed Graphs with One Language Model EncoderCode0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
Memory TransformerCode0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
Learning Longer Memory in Recurrent Neural NetworksCode0
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image ManipulationCode0
Training Vision-Language Models with Less Bimodal SupervisionCode0
Language Models can Self-Improve at State-Value Estimation for Better SearchCode0
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information MaximizationCode0
Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question MatchingCode0
The Quantization Model of Neural ScalingCode0
Linearized Relative Positional EncodingCode0
Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple DomainsCode0
From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language ModelsCode0
UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense DisambiguationCode0
Semi-supervised Sequence LearningCode0
Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models SolutionsCode0
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in ArithmeticCode0
OTCE: Hybrid SSM and Attention with Cross Domain Mixture of Experts to construct Observer-Thinker-Conceiver-ExpresserCode0
Towards Fully Bilingual Deep Language ModelingCode0
SynSUM -- Synthetic Benchmark with Structured and Unstructured Medical RecordsCode0
The Referential Reader: A Recurrent Entity Network for Anaphora ResolutionCode0
Ordered Neurons: Integrating Tree Structures into Recurrent Neural NetworksCode0
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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