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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 12101–12150 of 17610 papers

TitleStatusHype
Introducing Semantics into Speech Encoders—0
ED-FAITH: Evaluating Dialogue Summarization on Faithfulness—0
Grafting Pre-trained Models for Multimodal Headline Generation—0
ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering—0
Controllable Citation Sentence Generation with Language ModelsCode0
Towards a Mathematics Formalisation Assistant using Large Language Models—0
Replacing Language Model for Style TransferCode0
Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission PredictionCode0
Textual Data Augmentation for Patient Outcomes Prediction—0
Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language DetectionCode0
Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning—0
Using Persuasive Writing Strategies to Explain and Detect Health MisinformationCode0
DocuT5: Seq2seq SQL Generation with Table Documentation—0
BERT in Plutarch's Shadows—0
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information—0
The CRINGE Loss: Learning what language not to model—0
Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis—0
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue—0
MSDT: Masked Language Model Scoring Defense in Text DomainCode0
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model ControlCode0
Training self-supervised peptide sequence models on artificially chopped proteins—0
Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration—0
Adaptive Multi-Corpora Language Model Training for Speech Recognition—0
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition—0
Active Learning with Tabular Language Models—0
Self-conditioned Embedding Diffusion for Text Generation—0
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic—0
Third-Party Aligner for Neural Word AlignmentsCode0
Word Order Matters when you Increase Masking—0
Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency GapsCode0
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment AnalysisCode0
Complex Reading Comprehension Through Question Decomposition—0
Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following—0
Learning Semantic Textual Similarity via Topic-informed Discrete Latent VariablesCode0
Probing neural language models for understanding of words of estimative probability—0
Noisy Channel for Automatic Text Simplification—0
Suffix Retrieval-Augmented Language ModelingCode0
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language InferenceCode0
Measuring Progress on Scalable Oversight for Large Language Models—0
OSIC: A New One-Stage Image Captioner Coined—0
Probing Statistical Representations For End-To-End ASR—0
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in ArithmeticCode0
Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device—0
Transformers on Multilingual Clause-Level MorphologyCode0
Circling Back to Recurrent Models of Language—0
Generative Adversarial Training Can Improve Neural Language Models—0
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder—0
Internal Language Model Estimation based Adaptive Language Model Fusion for Domain Adaptation—0
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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