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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12601–12650 of 17610 papers

TitleStatusHype
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages—0
Mukayese: Turkish NLP Strikes Back—0
Predictive text for agglutinative and polysynthetic languages—0
Pinyin-bert: A new solution to Chinese pinyin to character conversion task—0
Error-Correcting Codes For Approximate Neural Sequence Prediction—0
CINO: A Chinese Minority Pre-trained Language Model—0
gaBERT — an Irish Language Model—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
Human Language Modeling—0
An Empirical Study of Finding Similar Exercises—0
Integrated Semantic and Phonetic Post-correction for Chinese Speech RecognitionCode0
Meeting Summarization with Pre-training and Clustering MethodsCode0
Interpreting Language Models Through Knowledge Graph ExtractionCode1
AUTOMATED AUDIO CAPTIONING BY FINE-TUNING BART WITH AUDIOSET TAGSCode0
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR—0
Joint Unsupervised and Supervised Training for Multilingual ASR—0
Semantically Grounded Object Matching for Robust Robotic Scene RearrangementCode0
Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming—0
iBOT: Image BERT Pre-Training with Online TokenizerCode1
Calculating Question Similarity is Enough: A New Method for KBQA Tasks—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
SocialBERT -- Transformers for Online SocialNetwork Language Modelling—0
Self-Normalized Importance Sampling for Neural Language Modeling—0
SynthBio: A Case Study in Human-AI Collaborative Curation of Text Datasets—0
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides—0
FPM: A Collection of Large-scale Foundation Pre-trained Language Models—0
Explaining Face Presentation Attack Detection Using Natural Language—0
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient FrameworkCode1
Machine-in-the-Loop Rewriting for Creative Image CaptioningCode0
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language ModelingCode1
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition—0
The neural architecture of language: Integrative modeling converges on predictive processingCode1
Lexically Aware Semi-Supervised Learning for OCR Post-CorrectionCode1
A text autoencoder from transformer for fast encoding language representation—0
CoreLM: Coreference-aware Language Model Fine-Tuning—0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
PhyloTransformer: A Discriminative Model for Mutation Prediction Based on a Multi-head Self-attention Mechanism—0
The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language ModelsCode1
An Explanation of In-context Learning as Implicit Bayesian InferenceCode1
Integrating Pretrained Language Model for Dialogue Policy Learning—0
Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model—0
A Fine-Grained Analysis of BERTScore—0
GTCOM Neural Machine Translation Systems for WMT21—0
Transfer Learning with Shallow Decoders: BSC at WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared TaskCode0
Small Model and In-Domain Data Are All You Need—0
Language Model Pretraining and Transfer Learning for Very Low Resource Languages—0
NICT Kyoto Submission for the WMT’21 Quality Estimation Task: Multimetric Multilingual Pretraining for Critical Error Detection—0
Compressive Performers in Language Modelling—0
ParsiNorm: A Persian Toolkit for Speech Processing NormalizationCode1
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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