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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9201–9250 of 17610 papers

TitleStatusHype
Learning Reward for Physical Skills using Large Language Model—0
Learning Rich Image Region Representation for Visual Question Answering—0
Learning Semantic Information from Raw Audio Signal Using Both Contextual and Phonetic Representations—0
Learning Semantic Representations in a Bigram Language Model—0
Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints—0
Learning Simpler Language Models with the Differential State Framework—0
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning—0
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition—0
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records—0
Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video—0
Learning the hyperparameters to learn morphology—0
Learning the Language of NVMe Streams for Ransomware Detection—0
Learning the Latent Rules of a Game from Data: A Chess Story—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds—0
Learning to Capitalize with Character-Level Recurrent Neural Networks: An Empirical Study—0
Learning to Compile Programs to Neural Networks—0
Learning to Compute Word Embeddings On the Fly—0
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling—0
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts—0
Learning to Define Terms in the Software Domain—0
Learning to Deliver: a Foundation Model for the Montreal Capacitated Vehicle Routing Problem—0
Learning to Diversify Neural Text Generation via Degenerative Model—0
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach—0
Learning to Complete Code with Sketches—0
Learning to Generate Long-term Future Narrations Describing Activities of Daily Living—0
Learning to Generate Text in Arbitrary Writing Styles—0
Learning to Generate Word Representations using Subword Information—0
Learning to Ground VLMs without Forgetting—0
Learning To Guide Human Decision Makers With Vision-Language Models—0
Learning to Guide Human Experts via Personalized Large Language Models—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Learning to Interpret and Describe Abstract Scenes—0
Learning to Interpret Natural Language Instructions—0
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding—0
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES—0
Learning to Plan Long-Term for Language Modeling—0
Learning to Predict from Textual Data—0
Learning to Prune: Context-Sensitive Pruning for Syntactic MT—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
Learning To Rank Resources with GNN—0
Learning to Reason at the Frontier of Learnability—0
Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning—0
Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models—0
Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data—0
Learning to Represent Image and Text with Denotation Graph—0
Learning To Retrieve Prompts for In-Context Learning—0
Learning to Sample Replacements for ELECTRA Pre-Training—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