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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12801–12850 of 17610 papers

TitleStatusHype
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task—0
Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency—0
Noise Injection Systemically Degrades Large Language Model Safety Guardrails—0
Noiser: Bounded Input Perturbations for Attributing Large Language Models—0
Noise-Robust ASR for the third 'CHiME' Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network—0
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge—0
Noisin: Unbiased Regularization for Recurrent Neural Networks—0
Noisy Channel for Automatic Text Simplification—0
Noisy Channel for Low Resource Grammatical Error Correction—0
Noisy Neural Language Modeling for Typing Prediction in BCI Communication—0
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model—0
No more hard prompts: SoftSRV prompting for synthetic data generation—0
LaPuda: LLM-Enabled Policy-Based Query Optimizer for Multi-modal Data—0
Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring—0
Non-autoregressive Transformer-based End-to-end ASR using BERT—0
Nondeterministic Stacks in Neural Networks—0
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models—0
No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge—0
No Need to Talk: Asynchronous Mixture of Language Models—0
No News is Good News: A Critique of the One Billion Word Benchmark—0
Non-iterative Parallel Text Generation via Glancing Transformer—0
Non-Linear Text Regression with a Deep Convolutional Neural Network—0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals—0
Nonparametric Bayesian Semi-supervised Word Segmentation—0
Noobs at Semeval-2021 Task 4: Masked Language Modeling for abstract answer prediction—0
Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models—0
Normalizador de Texto para Lingua Portuguesa baseado em Modelo de Linguagem (A Normalizer based on Language Model for Texts in Portuguese)[In Portuguese]—0
Normalized Log-Linear Interpolation of Backoff Language Models is Efficient—0
Normalizing Text using Language Modelling based on Phonetics and String Similarity—0
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes—0
Normalizing tweets with edit scripts and recurrent neural embeddings—0
NormFormer: Improved Transformer Pretraining with Extra Normalization—0
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model—0
NoteLLM: A Retrievable Large Language Model for Note Recommendation—0
Not Enough Data? Deep Learning to the Rescue!—0
Not Quite 'Ask a Librarian': AI on the Nature, Value, and Future of LIS—0
Not-so fine-tuning: Measures of Common Sense for Language Models—0
Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning—0
Novel Natural Language Summarization of Program Code via Leveraging Multiple Input Representations—0
Novel-WD: Exploring acquisition of Novel World Knowledge in LLMs Using Prefix-Tuning—0
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization—0
Novice Type Error Diagnosis with Natural Language Models—0
Nowcasting the euro area with social media data—0
Now It Sounds Like You: Learning Personalized Vocabulary On Device—0
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification—0
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache—0
NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction—0
NSP-BERT: A Prompt-based Few-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction—0
NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction—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