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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1200112050 of 17610 papers

TitleStatusHype
Data Augmentation for Biomedical Factoid Question AnsweringCode0
IDPG: An Instance-Dependent Prompt Generation Method0
Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model0
Contextual Representation Learning beyond Masked Language ModelingCode1
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text GenerationCode0
Characterizing and Understanding the Behavior of Quantized Models for Reliable DeploymentCode0
Fair and Argumentative Language Modeling for Computational ArgumentationCode0
BioBART: Pretraining and Evaluation of A Biomedical Generative Language ModelCode1
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms0
RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text miningCode1
MAESTRO: Matched Speech Text Representations through Modality Matching0
Music-robust Automatic Lyrics Transcription of Polyphonic MusicCode0
Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings0
Autoencoding Language Model Based Ensemble Learning for Commonsense Validation and Explanation0
Structure-aware Protein Self-supervised LearningCode1
IterVM: Iterative Vision Modeling Module for Scene Text RecognitionCode1
SecureBERT: A Domain-Specific Language Model for CybersecurityCode1
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition0
On the Effectiveness of Pretrained Models for API Learning0
PaLM: Scaling Language Modeling with PathwaysCode2
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual RetrievalCode0
SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language ModelCode0
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks0
Do As I Can, Not As I Say: Grounding Language in Robotic AffordancesCode2
An Analysis of Semantically-Aligned Speech-Text Embeddings0
Into-TTS : Intonation Template Based Prosody Control System0
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative StudyCode0
Automatic Dialect Density Estimation for African American English0
POS-BERT: Point Cloud One-Stage BERT Pre-TrainingCode1
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text GenerationCode1
Entity-Centric Query Refinement0
BERT-Assisted Semantic Annotation Correction for Emotion-Related QuestionsCode0
Feature Structure Distillation with Centered Kernel Alignment in BERT TransferringCode1
Syntax-informed Question Answering with Heterogeneous Graph Transformer0
NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction0
Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition0
Monarch: Expressive Structured Matrices for Efficient and Accurate TrainingCode1
Scaling Language Model Size in Cross-Device Federated Learning0
Generative Pre-Trained Transformers for Biologically Inspired Design0
ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices0
An Empirical Study of Language Model Integration for Transducer based Speech Recognition0
SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing TasksCode1
A 23 MW data centre is all you need0
PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech RepresentationsCode0
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language ModelCode0
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data0
Position-based Prompting for Health Outcome Generation0
Probing phoneme, language and speaker information in unsupervised speech representations0
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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