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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12351–12400 of 17610 papers

TitleStatusHype
Improving Code-switched ASR with Linguistic Information—0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning—0
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal ProductsCode0
Automatic Nominalization of Clauses—0
CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets—0
An Exploration of Prompt-Based Zero-Shot Relation Extraction Method—0
Automatic Detection of Borrowings in Low-Resource Languages of the Caucasus: Andic branch—0
Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
ARGUABLY@SMM4H’22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model—0
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
A Simple Model for Distantly Supervised Relation Extraction—0
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling—0
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?Code0
A Closer Look at Parameter Contributions When Training Neural Language and Translation Models—0
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model—0
ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation RecognitionCode0
Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network—0
Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun ResolutionCode0
SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data—0
Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling—0
Learning by Distilling Context—0
What Makes Pre-trained Language Models Better Zero-shot Learners?Code0
Unpacking Large Language Models with Conceptual Consistency—0
Toward Trustworthy Neural Program Synthesis—0
Few-shot Text Classification with Dual Contrastive Consistency—0
Bidirectional Language Models Are Also Few-shot Learners—0
Repairing Bugs in Python Assignments Using Large Language Models—0
Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus—0
Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models—0
Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer—0
Who is GPT-3? An Exploration of Personality, Values and DemographicsCode0
Breaking Time Invariance: Assorted-Time Normalization for RNNsCode0
Improving alignment of dialogue agents via targeted human judgements—0
ArNLI: Arabic Natural Language Inference for Entailment and Contradiction DetectionCode0
Entailment Semantics Can Be Extracted from an Ideal Language ModelCode0
End-to-End Lyrics Recognition with Self-supervised Learning—0
Paraphrasing Is All You Need for Novel Object Captioning—0
Learning Chess With Language Models and Transformers—0
Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity—0
LGDN: Language-Guided Denoising Network for Video-Language Modeling—0
Whodunit? Learning to Contrast for Authorship AttributionCode0
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccinesCode0
DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation—0
Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic—0
Prompting for a conversation: How to control a dialog model?—0
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language ModelCode0
WeLM: A Well-Read Pre-trained Language Model for Chinese—0
Relaxed Attention for Transformer Models—0
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging—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