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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9151–9200 of 17610 papers

TitleStatusHype
Learning Better Intent Representations for Financial Open Intent Classification—0
Learning Better Sentence Representation with Syntax Information—0
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs—0
Learning by Distilling Context—0
Learning Chess Blindfolded—0
Learning Chess With Language Models and Transformers—0
Learning Combinatorial Prompts for Universal Controllable Image Captioning—0
Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs—0
Learning Continuous User Representations through Hybrid Filtering with doc2vec—0
Learning Cross-Context Entity Representations from Text—0
Learning Cross-Lingual IR from an English Retriever—0
Learning Cross-lingual Representations for Event Coreference Resolution with Multi-view Alignment and Optimal Transport—0
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model—0
Learning Cross-lingual Word Embeddings via Matrix Co-factorization—0
Learning Determinantal Point Processes by Corrective Negative Sampling—0
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration—0
Learning Document Embeddings With CNNs—0
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation—0
Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes—0
Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories—0
Learning Evolution via Optimization Knowledge Adaptation—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
Learning Flexible Translation between Robot Actions and Language Descriptions—0
Enhancing Continual Learning with Global Prototypes: Counteracting Negative Representation Drift—0
Learning Free Token Reduction for Multi-Modal Large Language Models—0
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets—0
Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research—0
Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery—0
Learning from Synthetic Data for Visual Grounding—0
Training Language Models with Language Feedback—0
Learning from Post-Editing: Online Model Adaptation for Statistical Machine Translation—0
Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration—0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences—0
Learning higher-order sequential structure with cloned HMMs—0
Learning How Hard to Think: Input-Adaptive Allocation of LM Computation—0
Learning Human-Human Interactions in Images from Weak Textual Supervision—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
Learning Joint Representation of Human Motion and Language—0
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations—0
Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge—0
Learning Natural Language Generation from Scratch—0
Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines—0
Learning Novel Skills from Language-Generated Demonstrations—0
Learning Personalized Decision Support Policies—0
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation—0
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization—0
Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning—0
Learning Representations for Detecting Abusive Language—0
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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