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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12051–12100 of 17610 papers

TitleStatusHype
Better Transcription of UK Supreme Court Hearings—0
Improving astroBERT using Semantic Textual Similarity—0
Syntactic Substitutability as Unsupervised Dependency SyntaxCode0
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All—0
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification—0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Fine-tuning language models to find agreement among humans with diverse preferences—0
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language ModelsCode0
Continuous diffusion for categorical data—0
Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5—0
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
SKDBERT: Compressing BERT via Stochastic Knowledge Distillation—0
GPT-3-driven pedagogical agents for training children's curious question-asking skills—0
Comparison Study Between Token Classification and Sequence Classification In Text Classification—0
Bidirectional Representations for Low Resource Spoken Language Understanding—0
Using Selective Masking as a Bridge between Pre-training and Fine-tuning—0
Question Answering and Question Generation for Finnish—0
TorchScale: Transformers at Scale—0
Word-Level Representation From Bytes For Language Modeling—0
Coreference Resolution through a seq2seq Transition-Based System—0
HyperTuning: Toward Adapting Large Language Models without Back-propagation—0
Converge to the Truth: Factual Error Correction via Iterative Constrained EditingCode0
Knowledge Prompting for Few-shot Action Recognition—0
Retrieval-Augmented Multimodal Language Modeling—0
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model—0
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning—0
Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task LearningCode0
ClipCrop: Conditioned Cropping Driven by Vision-Language Model—0
Deanthropomorphising NLP: Can a Language Model Be Conscious?—0
AF Adapter: Continual Pretraining for Building Chinese Biomedical Language ModelCode0
Conceptor-Aided Debiasing of Large Language Models—0
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge—0
Leveraging per Image-Token Consistency for Vision-Language Pre-training—0
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity RetrievalCode0
Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter—0
Metadata Might Make Language Models Better—0
Protein language model rescue mutations highlight variant effects and structure in clinically relevant genesCode0
3d human motion generation from the text via gesture action classification and the autoregressive model—0
CAPE: Corrective Actions from Precondition Errors using Large Language Models—0
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer—0
Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE—0
Prompting PaLM for Translation: Assessing Strategies and Performance—0
TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task—0
Towards Computationally Verifiable Semantic Grounding for Language Models—0
Streaming Joint Speech Recognition and Disfluency DetectionCode0
Relationship of the language distance to English ability of a country—0
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use—0
Reasoning Circuits: Few-shot Multihop Question Generation with Structured Rationales—0
Empowering Language Models with Knowledge Graph Reasoning for Question Answering—0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers—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