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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 11351–11400 of 17610 papers

TitleStatusHype
Multi-Figurative Language GenerationCode1
Distilling the Knowledge of BERT for CTC-based ASR—0
Do Large Language Models know what humans know?Code0
Selective Text Augmentation with Word Roles for Low-Resource Text ClassificationCode0
Semantically Meaningful Metrics for Norwegian ASR SystemsCode0
Neural Approaches to Multilingual Information Retrieval—0
TransPolymer: a Transformer-based language model for polymer property predictionsCode1
Vision-Language Adaptive Mutual Decoder for OOV-STR—0
FOLIO: Natural Language Reasoning with First-Order LogicCode1
Prefix Embeddings for In-context Machine Translation—0
UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale RetrievalCode1
The Fellowship of the Authors: Disambiguating Names from Social Network Context—0
Continuous QA Learning with Structured Prompts—0
Efficient Sparsely Activated Transformers—0
To Adapt or to Fine-tune: A Case Study on Abstractive SummarizationCode0
Efficient and Interpretable Neural Models for Entity Tracking—0
Personal Attribute Prediction from ConversationsCode0
LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems—0
ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers—0
Bayesian Neural Network Language Modeling for Speech RecognitionCode0
On Unsupervised Training of Link Grammar Based Language Models—0
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context—0
Training a T5 Using Lab-sized Resources—0
DPTDR: Deep Prompt Tuning for Dense Passage RetrievalCode0
Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language ModelsCode0
Learning from Unlabeled 3D Environments for Vision-and-Language NavigationCode1
PEER: A Collaborative Language Model—0
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language ModelCode1
Repair Is Nearly Generation: Multilingual Program Repair with LLMs—0
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons LearnedCode3
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal AdaptationCode0
Prompting as Probing: Using Language Models for Knowledge Base ConstructionCode1
Learning Better Masking for Better Language Model Pre-trainingCode0
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution—0
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
Evaluate Confidence Instead of Perplexity for Zero-shot Commonsense Reasoning—0
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers—0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization—0
A Syntax Aware BERT for Identifying Well-Formed Queries in a Curriculum Framework—0
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation LearningCode1
Z-Code++: A Pre-trained Language Model Optimized for Abstractive SummarizationCode4
VLMAE: Vision-Language Masked Autoencoder—0
Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks—0
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes—0
VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social MediaCode1
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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