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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11551–11600 of 17610 papers

TitleStatusHype
An Empirical Study on Pseudo-log-likelihood Bias Measures for Masked Language Models Using Paraphrased Sentences—0
Unsupervised Paraphrasability Prediction for Compound Nominalizations—0
Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation—0
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection—0
You Don’t Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers’ Private PersonasCode0
Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text—0
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language ModelCode0
Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis—0
An Annotated Dataset and Automatic Approaches for Discourse Mode Identification in Low-resource Bengali Language—0
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models—0
Forecasting Future World Events with Neural NetworksCode1
BigBIO: A Framework for Data-Centric Biomedical Natural Language ProcessingCode2
"Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfer—0
Language model compression with weighted low-rank factorization—0
Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian TweetsCode0
esCorpius: A Massive Spanish Crawling Corpus—0
GSCLIP : A Framework for Explaining Distribution Shifts in Natural Language—0
Improving Visual Grounding by Encouraging Consistent Gradient-based ExplanationsCode1
GPTs at Factify 2022: Prompt Aided Fact-Verification—0
Improving Deliberation by Text-Only and Semi-Supervised Training—0
Contextual Density Ratio for Language Model Biasing of Sequence to Sequence ASR Systems—0
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User ReviewsCode0
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody—0
Solving Quantitative Reasoning Problems with Language ModelsCode2
Knowledge Distillation of Transformer-based Language Models Revisited—0
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction—0
CC-Riddle: A Question Answering Dataset of Chinese Character RiddlesCode1
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance GenerationCode1
Long Range Language Modeling via Gated State SpacesCode0
Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding—0
A Zero-Shot Classification Approach for a Word-Guessing Challenge—0
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One—0
Protoformer: Embedding Prototypes for TransformersCode1
TEVR: Improving Speech Recognition by Token Entropy Variance ReductionCode2
Construct a Sentence with Multiple Specified Words—0
Distilling a Pretrained Language Model to a Multilingual ASR ModelCode1
Evaluating Generative Patent Language Models—0
Mining Error Templates for Grammatical Error CorrectionCode2
Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging DataCode1
Efficient and effective training of language and graph neural network models—0
Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech—0
DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon—0
GODEL: Large-Scale Pre-Training for Goal-Directed DialogCode2
Using cognitive psychology to understand GPT-3—0
Knowledge Graph Fusion for Language Model Fine-tuning—0
Questions Are All You Need to Train a Dense Passage RetrieverCode1
Don't Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information—0
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic ParsingCode1
General Framework for Reversible Data Hiding in Texts Based on Masked Language Modeling—0
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP—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