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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13301–13350 of 17610 papers

TitleStatusHype
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models—0
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation—0
An Empirical Study of Finding Similar Exercises—0
Composable Sparse Fine-Tuning for Cross-Lingual Transfer—0
Improving Controllable Text Generation with Position-Aware Weighted Decoding—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
Heterogeneous Language Model Optimization in Automatic Speech Recognition—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level—0
How does the pre-training objective affect what large language models learn about linguistic properties?—0
IDPG: An Instance-Dependent Prompt Generation Method—0
DAML-ST5: Low Resource Style Transfer via Domain Adaptive Meta Learning—0
DAWSON: Data Augmentation using Weak Supervision On Natural Language—0
Human Language Modeling—0
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models—0
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis—0
Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification—0
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion—0
RE: A Study for Restorable Embeddings—0
Predictive text for agglutinative and polysynthetic languages—0
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models—0
Phrase-aware Unsupervised Constituency Parsing—0
Meeting Summarization with Pre-training and Clustering MethodsCode0
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages—0
Temporal Language Modeling for Short Text Document Classification with Transformers—0
MIMICause: Representation and automatic extraction of causal relation types from clinical notes—0
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification—0
Self-Distilled Pruning of Neural Networks—0
N-grammer: Augmenting Transformers with latent n-grams—0
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks—0
Leveraging Uni-Modal Self-Supervised Learning for Multimodal Audio-visual Speech Recognition—0
Tokenization on the Number Line is All You Need—0
Sentence-level Privacy for Document Embeddings—0
Text-to-Table: A New Way of Information Extraction—0
Predicting Attention Sparsity in Transformers—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer—0
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling—0
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data—0
Mukayese: Turkish NLP Strikes Back—0
Repetition Facilitates Processing: The Processing Advantage of Construction Repetition in Dialogue—0
Multi-Stage Prompting for Knowledgeable Dialogue Generation—0
Pinyin-bert: A new solution to Chinese pinyin to character conversion task—0
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings—0
Psych-E: Configurable Response Generation using Personality Traits and Pragmatics—0
Prompt-Learning for Fine-Grained Entity Typing—0
KALA: Knowledge-Augmented Language Model Adaptation—0
Meta-learning via Language Model In-context Tuning—0
On the Multilingual Capabilities of Very Large-Scale English Language Models—0
Language Model-Guided Knowledge Subgraphs for Question Answering—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