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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 12151–12200 of 17610 papers

TitleStatusHype
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language ModelsCode2
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State TrackingCode1
A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism—0
Mukayese: Turkish NLP Strikes BackCode1
Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering—0
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language ModelsCode1
"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction—0
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale—0
Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text RepresentationCode1
CINO: A Chinese Minority Pre-trained Language Model—0
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training—0
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification TasksCode1
Logical Fallacy DetectionCode1
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
Controllable Natural Language Generation with Contrastive Prefixes—0
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support ConversationCode1
A Systematic Evaluation of Large Language Models of CodeCode3
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence LearningCode1
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech RecognitionCode1
Oolong: Investigating What Makes Transfer Learning Hard with Controlled StudiesCode0
Probing BERT's priors with serial reproduction chainsCode0
From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics SystemsCode0
Korean Tokenization for Beam Search Rescoring in Speech Recognition—0
Evaluating Persian Tokenizers—0
VU-BERT: A Unified framework for Visual Dialog—0
Adaptive Discounting of Implicit Language Models in RNN-Transducers—0
StyleBERT: Chinese pretraining by font style information—0
Interpreting Language Models with Contrastive ExplanationsCode1
Transformer Quality in Linear TimeCode1
Contextual Semantic Embeddings for Ontology Subsumption PredictionCode2
Reward Modeling for Mitigating Toxicity in Transformer-based Language Models—0
From FreEM to D'AlemBERT: a Large Corpus and a Language Model for Early Modern French—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models—0
cosFormer: Rethinking Softmax in AttentionCode1
LAMP: Extracting Text from Gradients with Language Model PriorsCode1
Should You Mask 15% in Masked Language Modeling?Code1
CAREER: A Foundation Model for Labor Sequence DataCode1
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers—0
XFBoost: Improving Text Generation with Controllable Decoders—0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model—0
General-purpose, long-context autoregressive modeling with Perceiver ARCode2
Misinformation Detection in Social Media Video Posts—0
Text-Based Action-Model Acquisition for Planning—0
Neighborhood Contrastive Learning for Scientific Document Representations with Citation EmbeddingsCode1
CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming SequencesCode1
Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?Code4
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning—0
Punctuation restoration in Swedish through fine-tuned KB-BERT—0
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder—0
Show:102550
← PrevPage 244 of 353Next →
All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified