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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13951–14000 of 17610 papers

TitleStatusHype
Language Model Evaluation Beyond Perplexity—0
Verdi: Quality Estimation and Error Detection for Bilingual CorporaCode0
Tesseract: Parallelize the Tensor Parallelism Efficiently—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
Sentiment analysis in tweets: an assessment study from classical to modern text representation modelsCode0
Predictive Representation Learning for Language Modeling—0
Lightweight Cross-Lingual Sentence Representation LearningCode0
Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation—0
Generative Adversarial Imitation Learning for Empathy-based AI—0
Generative Text Modeling through Short Run InferenceCode0
Leveraging Linguistic Coordination in Reranking N-Best Candidates For End-to-End Response Selection Using BERT—0
On Privacy and Confidentiality of Communications in Organizational Graphs—0
SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis—0
Towards an IMU-based Pen Online Handwriting Recognizer—0
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains—0
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence LabelingCode0
Few-Shot Upsampling for Protest Size DetectionCode0
Pre-trained Language Model based Ranking in Baidu Search—0
LMSOC: An approach for socially sensitive pretraining—0
Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining—0
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning—0
GapPredict: A Language Model for Resolving Gaps in Draft Genome AssembliesCode0
See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization—0
VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding—0
Accelerating Gossip SGD with Periodic Global Averaging—0
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing—0
Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging—0
Sentence Similarity Based on Contexts—0
Neural Predictive Text for Grammatical Error Prevention—0
SINA-BERT: A Pre-Trained Language Model for Analysis of Medical Texts in Persian—0
Doc2Dict: Information Extraction as Text GenerationCode0
A Cognitive Regularizer for Language Modeling—0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
Towards Human-Free Automatic Quality Evaluation of German Summarization—0
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay—0
Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph CaptioningCode0
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech—0
Dispatcher: A Message-Passing Approach To Language ModellingCode0
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-TuningCode0
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language ModelsCode0
Computer-Aided Design as Language—0
Inferring the Reader: Guiding Automated Story Generation with Commonsense ReasoningCode0
HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish—0
Impact of Gender Debiased Word Embeddings in Language Modeling—0
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review—0
Unsupervised Document Expansion for Information Retrieval with Stochastic Text GenerationCode0
On the limit of English conversational speech recognition—0
Larger-Scale Transformers for Multilingual Masked Language Modeling—0
It’s Basically the Same Language Anyway: the Case for a Nordic Language Model—0
Measuring Translationese across Levels of Expertise: Are Professionals more Surprising than Students?—0
Show:102550
← PrevPage 280 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