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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1100111050 of 17610 papers

TitleStatusHype
Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge0
Evaluating context-invariance in unsupervised speech representationsCode0
Contrastive Decoding: Open-ended Text Generation as OptimizationCode2
COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust LearningCode1
SAN: a robust end-to-end ASR model architecture0
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model0
Retrieval Oriented Masking Pre-training Language Model for Dense Passage RetrievalCode2
Open-vocabulary Semantic Segmentation with Frozen Vision-Language ModelsCode1
Learning Joint Representation of Human Motion and Language0
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence LabelingCode0
What Language Model to Train if You Have One Million GPU Hours?Code3
Truncation Sampling as Language Model DesmoothingCode1
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design0
Will we run out of data? Limits of LLM scaling based on human-generated dataCode1
A Robust Bias Mitigation Procedure Based on the Stereotype Content ModelCode0
Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks0
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuningCode1
Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?0
N-gram Is Back: Residual Learning of Neural Text Generation with n-gram Language ModelCode1
Cloning Ideology and Style using Deep Learning0
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language ModelingCode0
MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR PredictionCode1
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition0
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios0
Learning Better Intent Representations for Financial Open Intent Classification0
Synthetic Text Generation with Differential Privacy: A Simple and Practical RecipeCode1
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models0
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence0
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry WritingCode1
Contrastive Search Is What You Need For Neural Text GenerationCode2
A single-cell gene expression language modelCode1
Dual Mechanism Priming Effects in Hindi Word Order0
Differentially Private Language Models for Secure Data Sharing0
Characterizing Verbatim Short-Term Memory in Neural Language ModelsCode0
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models0
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding TasksCode0
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text GenerationCode1
Towards Unifying Reference Expression Generation and ComprehensionCode0
A BERT-based Deep Learning Approach for Reputation Analysis in Social Media0
Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification0
Do Language Models Understand Measurements?0
Code4Struct: Code Generation for Few-Shot Event Structure PredictionCode1
Exploring the Value of Pre-trained Language Models for Clinical Named Entity RecognitionCode0
Language Model Pre-Training with Sparse Latent TypingCode1
LMPriors: Pre-Trained Language Models as Task-Specific Priors0
P^3LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training0
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding0
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data AugmentationCode0
Learning Vector-Quantized Item Representation for Transferable Sequential RecommendersCode1
Understanding Domain Learning in Language Models Through Subpopulation AnalysisCode0
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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