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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 31513200 of 17610 papers

TitleStatusHype
ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering SystemCode1
kNN-Prompt: Nearest Neighbor Zero-Shot InferenceCode1
Controllable Text Generation with Neurally-Decomposed OracleCode1
Quark: Controllable Text Generation with Reinforced UnlearningCode1
Training and Inference on Any-Order Autoregressive Models the Right WayCode1
Gradient-Based Constrained Sampling from Language ModelsCode1
Multimodal Knowledge Alignment with Reinforcement LearningCode1
NaturalProver: Grounded Mathematical Proof Generation with Language ModelsCode1
Training Language Models with Memory AugmentationCode1
Transcormer: Transformer for Sentence Scoring with Sliding Language ModelingCode1
Are Large Pre-Trained Language Models Leaking Your Personal Information?Code1
GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language ModelsCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in BanglaCode1
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language ModelsCode1
Prompt Tuning for Discriminative Pre-trained Language ModelsCode1
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and BeyondCode1
On Measuring Social Biases in Prompt-Based Multi-Task LearningCode1
The Geometry of Multilingual Language Model RepresentationsCode1
Language Models with Image Descriptors are Strong Few-Shot Video-Language LearnersCode1
Housekeep: Tidying Virtual Households using Commonsense ReasoningCode1
DeepStruct: Pretraining of Language Models for Structure PredictionCode1
KERPLE: Kernelized Relative Positional Embedding for Length ExtrapolationCode1
Visually-Augmented Language ModelingCode1
RankGen: Improving Text Generation with Large Ranking ModelsCode1
TIE: Topological Information Enhanced Structural Reading Comprehension on Web PagesCode1
Weakly Supervised Text Classification using Supervision Signals from a Language ModelCode1
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language ModelingCode1
Localized Vision-Language Matching for Open-vocabulary Object DetectionCode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More EffectiveCode1
Extracting Latent Steering Vectors from Pretrained Language ModelsCode1
Human Language ModelingCode1
Vietnamese Automatic Speech Recognition using Wav2vec 2.0Code1
Multimodal Semi-Supervised Learning for Text RecognitionCode1
The Unreliability of Explanations in Few-shot Prompting for Textual ReasoningCode1
FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging FrameworkCode1
Declaration-based Prompt Tuning for Visual Question AnsweringCode1
A Simple Contrastive Learning Objective for Alleviating Neural Text DegenerationCode1
Contrastive Learning for Prompt-Based Few-Shot Language LearnersCode1
Entity-aware Transformers for Entity SearchCode1
Mix and Match: Learning-free Controllable Text Generationusing Energy Language ModelsCode1
OPI@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text using RoBERTa Pre-trained Language ModelsCode1
Thai Nested Named Entity Recognition CorpusCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural NetworksCode1
-former: Infinite Memory TransformerCode1
Unsupervised Dependency Graph NetworkCode1
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics GraphCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
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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