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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7901–7950 of 17610 papers

TitleStatusHype
The Go Transformer: Natural Language Modeling for Game Play—0
The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances—0
The GUA-Speech System Description for CNVSRC Challenge 2023—0
The Hydra Effect: Emergent Self-repair in Language Model Computations—0
The IBM 2015 English Conversational Telephone Speech Recognition System—0
The IBM 2016 English Conversational Telephone Speech Recognition System—0
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task—0
The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It—0
The Impact of Depth on Compositional Generalization in Transformer Language Models—0
The Impact of Explanations on AI Competency Prediction in VQA—0
The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4—0
The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers—0
The Impact of Token Granularity on the Predictive Power of Language Model Surprisal—0
The Importance of Context in Very Low Resource Language Modeling—0
The Importance of Generation Order in Language Modeling—0
The Importance of Prompt Tuning for Automated Neuron Explanations—0
The Importance of the Current Input in Sequence Modeling—0
The INCOMSLAV Platform: Experimental Website with Integrated Methods for Measuring Linguistic Distances and Asymmetries in Receptive Multilingualism—0
The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis—0
The Information of Large Language Model Geometry—0
The Inside-Outside Recursive Neural Network model for Dependency Parsing—0
The Intelius Nickname Collection: Quantitative Analyses from Billions of Public Records—0
The Zero Resource Speech Challenge 2021: Spoken language modelling—0
The Invalsi Benchmarks: measuring Linguistic and Mathematical understanding of Large Language Models in Italian—0
The IOIT English ASR system for IWSLT 2016—0
The JHU Machine Translation Systems for WMT 2017—0
The JHU Machine Translation Systems for WMT 2016—0
The JHU Parallel Corpus Filtering Systems for WMT 2018—0
The Karlsruhe Institute of Technology Translation Systems for the WMT 2015—0
The Karlsruhe Institute of Technology Translation Systems for the WMT 2012—0
The Karlsruhe Institute of Technology Translation Systems for the WMT 2014—0
The KIT-LIMSI Translation System for WMT 2014—0
The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation—0
The Large Language Model GreekLegalRoBERTa—0
The LIG system for the English-Czech Text Translation Task of IWSLT 2019—0
The Limitations of Limited Context for Constituency Parsing—0
The Lipschitz Constant of Self-Attention—0
THELMA: Task Based Holistic Evaluation of Large Language Model Applications-RAG Question Answering—0
The LMU Munich Unsupervised Machine Translation Systems—0
The LMU Munich Unsupervised Machine Translation System for WMT19—0
The Magnitude of Categories of Texts Enriched by Language Models—0
The Marchex 2018 English Conversational Telephone Speech Recognition System—0
Thematic Analysis with Large Language Models: does it work with languages other than English? A targeted test in Italian—0
Theme-driven Keyphrase Extraction to Analyze Social Media Discourse—0
The Method for Storing Patterns in Neural Networks-Memorization and Recall of QR code Patterns-—0
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition—0
The Microsoft 2016 Conversational Speech Recognition System—0
The Microsoft 2017 Conversational Speech Recognition System—0
The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI—0
The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation—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