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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1210112150 of 17610 papers

TitleStatusHype
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet ExtractionCode1
TA-SBERT: Token Attention Sentence-BERT for Improving Sentence Representation0
Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive ControlCode0
Multi-Stage Prompting for Knowledgeable Dialogue Generation0
Memorizing TransformersCode2
Pseudo-Q: Generating Pseudo Language Queries for Visual GroundingCode1
KinyaBERT: a Morphology-aware Kinyarwanda Language ModelCode1
In-Context Learning for Few-Shot Dialogue State TrackingCode1
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure0
Geographic Adaptation of Pretrained Language ModelsCode0
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training DataCode1
Evaluating the Text-to-SQL Capabilities of Large Language Models0
Training a Tokenizer for Free with Private Federated Learning0
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL0
Representation Learning for Resource-Constrained Keyphrase GenerationCode1
ReACC: A Retrieval-Augmented Code Completion FrameworkCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective DistillationCode0
Do Language Models Plagiarize?Code1
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer0
PERT: Pre-training BERT with Permuted Language ModelCode2
Efficient Language Modeling with Sparse all-MLP0
All in One: Exploring Unified Video-Language Pre-trainingCode2
Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation0
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing AnomaliesCode1
Block-Recurrent TransformersCode2
Are discrete units necessary for Spoken Language Modeling?0
Compilable Neural Code Generation with Compiler Feedback0
Internet-augmented language models through few-shot prompting for open-domain question answering0
Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide0
MVP: Multimodality-guided Visual Pre-training0
Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition0
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing0
A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling0
LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings0
Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC DomainCode1
Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing0
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks0
Which side are you on? Insider-Outsider classification in conspiracy-theoretic social media0
Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding0
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding0
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models0
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
Leveraging Pre-trained BERT for Audio Captioning0
Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models0
Deep Lexical Hypothesis: Identifying personality structure in natural language0
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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