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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1130111350 of 17610 papers

TitleStatusHype
Mega: Moving Average Equipped Gated AttentionCode2
WeLM: A Well-Read Pre-trained Language Model for Chinese0
Generate rather than Retrieve: Large Language Models are Strong Context GeneratorsCode2
PromptCast: A New Prompt-based Learning Paradigm for Time Series ForecastingCode1
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging0
Probabilistic Generative Transformer Language models for Generative Design of MoleculesCode1
Relaxed Attention for Transformer Models0
Automatic Label Sequence Generation for Prompting Sequence-to-sequence ModelsCode1
A Few-shot Approach to Resume Information Extraction via PromptsCode0
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes0
GAMA: Generative Adversarial Multi-Object Scene AttacksCode1
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level TransferCode1
From Disfluency Detection to Intent Detection and Slot FillingCode0
CodeQueries: A Dataset of Semantic Queries over CodeCode1
Selective Token Generation for Few-shot Natural Language GenerationCode0
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained DecodingCode1
NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning0
Can Offline Reinforcement Learning Help Natural Language Understanding?0
PTab: Using the Pre-trained Language Model for Modeling Tabular Data0
Stateful Memory-Augmented Transformers for Efficient Dialogue ModelingCode0
TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at TwitterCode1
uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers0
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation ApproachCode1
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks0
Out of One, Many: Using Language Models to Simulate Human Samples0
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation0
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking ModelsCode1
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models0
Improving Language Model Prompting in Support of Semi-autonomous Task Learning0
Exploring Code Style Transfer with Neural Networks0
Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption ContestCode1
Revisiting Neural Scaling Laws in Language and Vision0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
Open-Domain Dialog Evaluation using Follow-Ups LikelihoodCode0
Applying wav2vec2 for Speech Recognition on Bengali Common Voices Dataset0
A Complex Network based Graph Embedding Method for Link Prediction0
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue0
T-NER: An All-Round Python Library for Transformer-based Named Entity RecognitionCode2
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages0
Multi-Granularity Prediction for Scene Text RecognitionCode0
Pre-Training a Graph Recurrent Network for Language RepresentationCode0
Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LMCode1
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language ModelCode0
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot ApproachCode0
Blessing of Class Diversity in Pre-training0
AudioLM: a Language Modeling Approach to Audio GenerationCode7
On the Effectiveness of Compact Biomedical TransformersCode1
ASR2K: Speech Recognition for Around 2000 Languages without AudioCode1
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach0
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
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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