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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4201–4225 of 17610 papers

TitleStatusHype
CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation—0
CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds—0
CLaC-BP at SemEval-2021 Task 8: SciBERT Plus Rules for MeasEval—0
CLaC @ QATS: Quality Assessment for Text Simplification—0
ClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models—0
Claim Verification using a Multi-GAN based Model—0
A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models—0
CLAIR: Evaluating Image Captions with Large Language Models—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models—0
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech—0
CLAP-ART: Automated Audio Captioning with Semantic-rich Audio Representation Tokenizer—0
Clarifying Implicit and Underspecified Phrases in Instructional Text—0
Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement—0
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision—0
Class-Based Language Modeling for Translating into Morphologically Rich Languages—0
Class-based LSTM Russian Language Model with Linguistic Information—0
Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation—0
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue—0
Classification Error Bound for Low Bayes Error Conditions in Machine Learning—0
Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks—0
Classification of Geological Borehole Descriptions Using a Domain Adapted Large Language Model—0
Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers—0
Classifying ASR Transcriptions According to Arabic Dialect—0
Classifying complex documents: comparing bespoke solutions to large language models—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