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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1190111950 of 17610 papers

TitleStatusHype
ChipSong: A Controllable Lyric Generation System for Chinese Popular SongCode0
Answer-level Calibration for Free-form Multiple Choice Question AnsweringCode0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model0
A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model0
Dataset Debt in Biomedical Language Modeling0
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level0
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual TemplatesCode0
Domain Knowledge Transferring for Pre-trained Language Model via Calibrated Activation Boundary DistillationCode0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders0
Domain-specific knowledge distillation yields smaller and better models for conversational commerce0
Combining Extraction and Generation for Constructing Belief-Consequence Causal Links0
Adaptive Differential Privacy for Language Model Training0
Debiasing Pre-Trained Language Models via Efficient Fine-TuningCode0
A Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation0
Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model0
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation0
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings0
Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 20200
Using ASR-Generated Text for Spoken Language Modeling0
Understanding BERT’s Mood: The Role of Contextual-Embeddings as User-Representations for Depression Assessment0
Large-Scale Multi-Document Summarization with Information Extraction and Compression0
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
Visualizing and Explaining Language Models0
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers0
LayoutBERT: Masked Language Layout Model for Object Insertion0
Self-Programming Artificial Intelligence Using Code-Generating Language Models0
C3-STISR: Scene Text Image Super-resolution with Triple CluesCode1
Flamingo: a Visual Language Model for Few-Shot LearningCode4
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining0
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak SupervisionCode1
Training Language Models with Language Feedback0
Vision-Language Pre-Training for Boosting Scene Text DetectorsCode0
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text ClassificationCode1
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model0
RigoBERTa: A State-of-the-Art Language Model For Spanish0
Probing Simile Knowledge from Pre-trained Language ModelsCode0
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS MetathesaurusCode0
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response GenerationCode1
A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical TransformerCode0
You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private PersonasCode0
Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors0
Parkinson's disease diagnostics using AI and natural language knowledge transfer0
Efficient Machine Translation Domain AdaptationCode0
GypSum: Learning Hybrid Representations for Code SummarizationCode1
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided MaskingCode0
C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval0
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference0
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials0
Super-Prompting: Utilizing Model-Independent Contextual Data to Reduce Data Annotation Required in Visual Commonsense Tasks0
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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