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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1390113950 of 17610 papers

TitleStatusHype
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval0
Extracting Training Data from Large Language ModelsCode1
MiniVLM: A Smaller and Faster Vision-Language Model0
CogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation IdentificationCode0
AffectON: Incorporating Affect Into Dialog Generation0
Mapping the Timescale Organization of Neural Language Models0
Morphology Matters: A Multilingual Language Modeling AnalysisCode0
Multi-Sense Language Modelling0
Towards Neural Programming InterfacesCode1
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR0
Fusing Context Into Knowledge Graph for Commonsense Question AnsweringCode1
Incorporating Domain Knowledge To Improve Topic Segmentation Of Long MOOC Lecture Videos0
Cross-lingual Transfer of Abstractive Summarizer to Less-resource Language0
Parameter Efficient Multimodal Transformers for Video Representation Learning0
TAP: Text-Aware Pre-training for Text-VQA and Text-CaptionCode1
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning0
UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2Code1
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream TasksCode1
Playing Text-Based Games with Common Sense0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning0
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning0
Federated Learning for Personalized Humor Recognition0
GottBERT: a pure German Language Model0
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition0
Cross-Loss Influence Functions to Explain Deep Network RepresentationsCode0
A Framework and Dataset for Abstract Art Generation via CalligraphyGAN0
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains0
ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers0
Speech Disfluencies occur at Higher Perplexities0
Towards Generating Query to Perform Query Focused Abstractive Summarization using Pre-trained ModelCode0
Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning0
Exploring Looping Effects in RNN-based Architectures0
A Sentiment-annotated Dataset of English Causal ConnectivesCode0
PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning MethodsCode3
Merge and Recognize: A Geometry and 2D Context Aware Graph Model for Named Entity Recognition from Visual Documents0
End-to-End Automatic Speech Recognition for GujaratiCode1
BertAA : BERT fine-tuning for Authorship Attribution0
CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels0
Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature0
Composing Byte-Pair Encodings for Morphological Sequence ClassificationCode0
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations0
MultiVitaminBooster at PARSEME Shared Task 2020: Combining Window- and Dependency-Based Features with Multilingual Contextualised Word Embeddings for VMWE Detection0
Surface Realization Using Pretrained Language Models0
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations0
Language Model Transformers as Evaluators for Open-domain DialoguesCode0
Neural Language Modeling for Named Entity Recognition0
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification FrameworkCode1
Scale down Transformer by Grouping Features for a Lightweight Character-level Language ModelCode1
Monolingual and Multilingual Reduction of Gender Bias in Contextualized RepresentationsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified