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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16351–16400 of 17610 papers

TitleStatusHype
Joint CTC/attention decoding for end-to-end speech recognition—0
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)—0
Automatically Generating Rhythmic Verse with Neural Networks—0
Detecting annotation noise in automatically labelled data—0
Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding—0
Improved Word Representation Learning with SememesCode0
Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations—0
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation—0
Generative Bridging Network in Neural Sequence Prediction—0
Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques in Statistical Language Model of Bahasa Indonesia—0
Device Placement Optimization with Reinforcement LearningCode0
Exploring the Syntactic Abilities of RNNs with Multi-task LearningCode0
YellowFin and the Art of Momentum TuningCode0
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LMCode0
Gated Recurrent Neural Tensor Network—0
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning—0
Visual attention models for scene text recognition—0
當代非監督式方法之比較於節錄式語音摘要 (An Empirical Comparison of Contemporary Unsupervised Approaches for Extractive Speech Summarization) [In Chinese]—0
Learning to Compute Word Embeddings On the Fly—0
Adversarial Generation of Natural Language—0
Biased Importance Sampling for Deep Neural Network TrainingCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
Who's to say what's funny? A computer using Language Models and Deep Learning, That's Who!—0
Semi-Supervised Model Training for Unbounded Conversational Speech Recognition—0
Deriving Neural Architectures from Sequence and Graph Kernels—0
Fast-Slow Recurrent Neural NetworksCode0
Unbiasing Truncated Backpropagation Through Time—0
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition—0
Spelling Correction as a Foreign Language—0
Recurrent Additive NetworksCode0
Mixed Membership Word Embeddings for Computational Social Science—0
Information Density as a Factor for Variation in the Embedding of Relative Clauses—0
Generating Memorable Mnemonic Encodings of NumbersCode0
Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion—0
Going Wider: Recurrent Neural Network With Parallel Cells—0
Finnish resources for evaluating language model semanticsCode0
Exploring Properties of Intralingual and Interlingual Association Measures Visually—0
The Effect of Translationese on Tuning for Statistical Machine Translation—0
From Characters to Words to in Between: Do We Capture Morphology?—0
Topically Driven Neural Language ModelCode0
Semi-supervised Multitask Learning for Sequence LabelingCode1
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition—0
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling—0
Affect-LM: A Neural Language Model for Customizable Affective Text Generation—0
Improving Context Aware Language ModelsCode0
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and MappingCode0
Character-Word LSTM Language Models—0
Bayesian Recurrent Neural NetworksCode1
Weakly Supervised Dense Video Captioning—0
Rhetorical relations for information retrieval—0
Show:102550
← PrevPage 328 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