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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10401–10450 of 17610 papers

TitleStatusHype
NeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs—0
Neutral residues: revisiting adapters for model extension—0
NeutraSum: A Language Model can help a Balanced Media Diet by Neutralizing News Summaries—0
Nevermind: Instruction Override and Moderation in Large Language Models—0
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions—0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
New Baseline in Automatic Speech Recognition for Northern S\'ami—0
New Directions in Vector Space Models of Meaning—0
New Intent Discovery with Attracting and Dispersing Prototype—0
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application—0
NewsEdits 2.0: Learning the Intentions Behind Updating News—0
NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks—0
New Textual Corpora for Serbian Language Modeling—0
Next Word Suggestion using Graph Neural Network—0
Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision—0
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding—0
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics—0
N-gram-based Tense Models for Statistical Machine Translation—0
N-gram Counts and Language Models from the Common Crawl—0
N-gram Language Modeling using Recurrent Neural Network Estimation—0
N-gram language models for massively parallel devices—0
N-grammer: Augmenting Transformers with latent n-grams—0
N-gram Model for Chinese Grammatical Error Diagnosis—0
N-gram Prediction and Word Difference Representations for Language Modeling—0
N-grams Bayesian Differential Privacy—0
NICT Kyoto Submission for the WMT’20 Quality Estimation Task: Intermediate Training for Domain and Task Adaptation—0
NICT Kyoto Submission for the WMT’21 Quality Estimation Task: Multimetric Multilingual Pretraining for Critical Error Detection—0
NIFTY Financial News Headlines Dataset—0
niksss at SemEval-2022 Task 6: Are Traditionally Pre-Trained Contextual Embeddings Enough for Detecting Intended Sarcasm ?—0
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
NILC-SWORNEMO at the Surface Realization Shared Task: Exploring Syntax-Based Word Ordering using Neural Models—0
NJU’s submission to the WMT20 QE Shared Task—0
Abstract Operations Research Modeling Using Natural Language Inputs—0
NL-Eye: Abductive NLI for Images—0
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis—0
NLP for Knowledge Discovery and Information Extraction from Energetics Corpora—0
NLP-PINGAN-TECH @ CL-SciSumm 2020—0
NLP Service APIs and Models for Efficient Registration of New Clients—0
NLP Service APIs and Models for Efficient Registration of New Clients—0
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection—0
nmT5 -- Is parallel data still relevant for pre-training massively multilingual language models?—0
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?—0
NN-grams: Unifying neural network and n-gram language models for Speech Recognition—0
No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
Noise-Based Regularizers for Recurrent Neural Networks—0
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
Show:102550
← PrevPage 209 of 353Next →
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