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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1165111700 of 17610 papers

TitleStatusHype
Offline RL for Natural Language Generation with Implicit Language Q LearningCode2
Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon0
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning0
Automatic Generation of Programming Exercises and Code Explanations using Large Language Models0
Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple MetricCode0
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning0
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code0
BayesFormer: Transformer with Uncertainty Estimation0
VL-BEiT: Generative Vision-Language Pretraining0
Development and Evaluation of Speech Recognition for the Welsh Language0
Automatic Speech Recognition for Irish: the ABAIR-ÉIST System0
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports0
PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on TwitterCode1
Multilingual Comparative Analysis of Deep-Learning Dependency Parsing Results Using Parallel Corpora0
Sentiment Analysis of Homeric Text: The 1st Book of Iliad0
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish0
Nepali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for Nepali Text Classification0
Latvian National Corpora Collection – Korpuss.lv0
Simple Tagging System with RoBERTa for Ancient Chinese0
SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models0
SMASH at Qur’an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering ScenariosCode0
LARSA22 at Qur’an QA 2022: Text-to-Text Transformer for Finding Answers to Questions from Qur’an0
Word Class Based Language Modeling: A Case of Upper Sorbian0
Data Augmentation for the Post-Stroke Speech Transcription (PSST) Challenge: Sometimes Less Is More0
IgboBERT Models: Building and Training Transformer Models for the Igbo LanguageCode0
Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey0
A Language Model for Spell Checking of Educational Texts in Kurdish (Sorani)Code0
HADREB: Human Appraisals and (English) Descriptions of Robot Emotional Behaviors0
CxLM: A Construction and Context-aware Language Model0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model0
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts0
A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text0
Discovering Financial Hypernyms by Prompting Masked Language Models0
Conversational Speech Recognition Needs Data? Experiments with Austrian German0
CHILLAX - at Arabic Hate Speech 2022: A Hybrid Machine Learning and Transformers based Model to Detect Arabic Offensive and Hate Speech0
HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish0
Developing Language Resources and NLP Tools for the North Korean Language0
ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot0
gaBERT — an Irish Language Model0
Automatic Translation Alignment for Ancient Greek and LatinCode0
Combination of Contextualized and Non-Contextualized Layers for Lexical Substitution in French0
Evaluating Pre-Trained Language Models for Focused Terminology Extraction from Swedish Medical Records0
FQuAD2.0: French Question Answering and Learning When You Don’t Know0
A Unifying View On Task-oriented Dialogue AnnotationCode0
Error Correction Environment for the Polish Parliamentary Corpus0
Clarifying Implicit and Underspecified Phrases in Instructional Text0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian0
Do Transformer Networks Improve the Discovery of Rules from Text?0
From FreEM to D’AlemBERT: a Large Corpus and a Language Model for Early Modern French0
Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification0
Show:102550
← PrevPage 234 of 353Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified