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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1265112700 of 17610 papers

TitleStatusHype
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning0
Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple MetricCode0
BayesFormer: Transformer with Uncertainty Estimation0
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code0
VL-BEiT: Generative Vision-Language Pretraining0
Word Class Based Language Modeling: A Case of Upper Sorbian0
Nepali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for Nepali Text Classification0
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish0
Simple Tagging System with RoBERTa for Ancient Chinese0
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples0
SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language SpecificationsCode0
Towards an Open-Source Dutch Speech Recognition System for the Healthcare Domain0
Multilingual and Multimodal Learning for Brazilian Portuguese0
SPOCK at FinCausal 2022: Causal Information Extraction Using Span-Based and Sequence Tagging Models0
LARSA22 at Qur’an QA 2022: Text-to-Text Transformer for Finding Answers to Questions from Qur’an0
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining0
Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words0
Sentiment Analysis of Homeric Text: The 1st Book of Iliad0
Tracking Changes in ESG Representation: Initial Investigations in UK Annual Reports0
Korean Language Modeling via Syntactic Guide0
Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish0
Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients0
Latvian National Corpora Collection – Korpuss.lv0
Multilingual Comparative Analysis of Deep-Learning Dependency Parsing Results Using Parallel Corpora0
SMASH at Qur’an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering ScenariosCode0
Transformer with Fourier Integral Attentions0
IgboBERT Models: Building and Training Transformer Models for the Igbo LanguageCode0
Evaluating Pre-Trained Language Models for Focused Terminology Extraction from Swedish Medical Records0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian0
Automatic Speech Recognition for Irish: the ABAIR-ÉIST System0
From FreEM to D’AlemBERT: a Large Corpus and a Language Model for Early Modern French0
HADREB: Human Appraisals and (English) Descriptions of Robot Emotional Behaviors0
Do Transformer Networks Improve the Discovery of Rules from Text?0
Conversational Speech Recognition Needs Data? Experiments with Austrian German0
Evaluating Methods for Extraction of Aspect Terms in Opinion Texts in Portuguese - the Challenges of Implicit AspectsCode0
A Unifying View On Task-oriented Dialogue AnnotationCode0
Automatic Word Segmentation and Part-of-Speech Tagging of Ancient Chinese Based on BERT Model0
ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot0
FQuAD2.0: French Question Answering and Learning When You Don’t Know0
HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish0
Development and Evaluation of Speech Recognition for the Welsh Language0
Automatic Translation Alignment for Ancient Greek and LatinCode0
Clarifying Implicit and Underspecified Phrases in Instructional Text0
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding0
Electoral Agitation Dataset: The Use Case of the Polish Election0
Combination of Contextualized and Non-Contextualized Layers for Lexical Substitution in French0
CHILLAX - at Arabic Hate Speech 2022: A Hybrid Machine Learning and Transformers based Model to Detect Arabic Offensive and Hate Speech0
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts0
BanglaHateBERT: BERT for Abusive Language Detection in Bengali0
CxLM: A Construction and Context-aware Language Model0
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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