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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10901–10950 of 17610 papers

TitleStatusHype
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model ControlCode0
The CRINGE Loss: Learning what language not to model—0
MSDT: Masked Language Model Scoring Defense in Text DomainCode0
ADEPT: A DEbiasing PrompT FrameworkCode1
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information—0
BERT in Plutarch's Shadows—0
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue—0
Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis—0
Training self-supervised peptide sequence models on artificially chopped proteins—0
Adaptive Multi-Corpora Language Model Training for Speech Recognition—0
BLOOM: A 176B-Parameter Open-Access Multilingual Language ModelCode4
FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration—0
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions—0
Word Order Matters when you Increase Masking—0
Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency GapsCode0
Active Learning with Tabular Language Models—0
Third-Party Aligner for Neural Word AlignmentsCode0
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic—0
Self-conditioned Embedding Diffusion for Text Generation—0
Investigating Fairness Disparities in Peer Review: A Language Model Enhanced ApproachCode1
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment AnalysisCode0
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Complex Reading Comprehension Through Question Decomposition—0
Probing neural language models for understanding of words of estimative probability—0
Learning Semantic Textual Similarity via Topic-informed Discrete Latent VariablesCode0
Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following—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
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingCode1
Measuring Progress on Scalable Oversight for Large Language Models—0
KGLM: Integrating Knowledge Graph Structure in Language Models for Link PredictionCode1
OSIC: A New One-Stage Image Captioner Coined—0
Using Large Pre-Trained Language Model to Assist FDA in Premarket Medical Device—0
LMentry: A Language Model Benchmark of Elementary Language TasksCode1
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in ArithmeticCode0
Probing Statistical Representations For End-To-End ASR—0
Open-Vocabulary Argument Role Prediction for Event ExtractionCode1
Fine-Tuning Language Models via Epistemic Neural NetworksCode1
Transformers on Multilingual Clause-Level MorphologyCode0
Contextual information integration for stance detection via cross-attentionCode1
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks AdaptivelyCode1
Circling Back to Recurrent Models of Language—0
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language ModelCode1
Generative Adversarial Training Can Improve Neural Language Models—0
Numerical Optimizations for Weighted Low-rank Estimation on Language Model—0
Towards Zero-Shot Code-Switched Speech Recognition—0
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model—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