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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9376–9400 of 17610 papers

TitleStatusHype
Lexical Selection for Hybrid MT with Sequence Labeling—0
Lexicon-Free Conversational Speech Recognition with Neural Networks—0
Lexinvariant Language Models—0
LFOSum: Summarizing Long-form Opinions with Large Language Models—0
A General Framework for Load Forecasting based on Pre-trained Large Language Model—0
LGDN: Language-Guided Denoising Network for Video-Language Modeling—0
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing—0
LIC-GAN: Language Information Conditioned Graph Generative GAN Model—0
LICO: Large Language Models for In-Context Molecular Optimization—0
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding—0
Lifelong Event Detection via Optimal Transport—0
Continuous QA Learning with Structured Prompts—0
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers—0
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora—0
LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes—0
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers—0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
LightLLM: A Versatile Large Language Model for Predictive Light Sensing—0
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning—0
LightPAL: Lightweight Passage Retrieval for Open Domain Multi-Document Summarization—0
LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph—0
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks—0
Lightweight Adaptive Mixture of Neural and N-gram Language Models—0
Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer—0
CCPL: Cross-modal Contrastive Protein Learning—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