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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12551–12600 of 17610 papers

TitleStatusHype
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data—0
Predicting Attention Sparsity in Transformers—0
RE: A Study for Restorable Embeddings—0
MATHion: Solving Math Word Problems with Logically Consistent Problems—0
Self-Distilled Pruning of Neural Networks—0
KALA: Knowledge-Augmented Language Model Adaptation—0
Psych-E: Configurable Response Generation using Personality Traits and Pragmatics—0
KinyaBERT: a Morphology-aware Kinyarwanda Language Model—0
Repetition Facilitates Processing: The Processing Advantage of Construction Repetition in Dialogue—0
Language Model-Guided Knowledge Subgraphs for Question Answering—0
Prix-LM: Pretraining for Multilingual Knowledge Base Construction—0
Probing BERT’s priors with serial reproduction chains—0
On a Benefit of Masked Language Model Pretraining: Robustness to Simplicity Bias—0
Sentence-level Privacy for Document Embeddings—0
Multi-Stage Prompting for Knowledgeable Dialogue Generation—0
Prompting as Multimodal Fusing—0
N-grammer: Augmenting Transformers with latent n-grams—0
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion—0
Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis—0
GLM: General Language Model Pretraining with Autoregressive Blank Infilling—0
DAWSON: Data Augmentation using Weak Supervision On Natural Language—0
IDPG: An Instance-Dependent Prompt Generation Method—0
Heterogeneous Language Model Optimization in Automatic Speech Recognition—0
Accurate, yet Inconsistent? Consistency Analysis on Language Models—0
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis—0
Extracting and Inferring Personal Attributes from Dialogue—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
Contrastive Conditional Masked Language Model for Non-autoregressive Neural Machine Translation—0
Controllable Natural Language Generation with Contrastive Prefixes—0
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference—0
Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification—0
ANNA: Enhanced Language Representation for Question Answering—0
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning—0
Evaluating the Text-to-SQL Capabilities of Large Language Models—0
Adaptive Testing and Debugging of NLP Models—0
Autoregressive Language Model for Zero-shot Constrained Keyphrase Generation—0
Can Language Models Be Specific? How?—0
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models—0
Composable Sparse Fine-Tuning for Cross-Lingual Transfer—0
BERT got a Date: Introducing Transformers to Temporal Tagging—0
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation—0
Composing Structure-Aware Batches for Pairwise Sentence Classification—0
Freezing the Pivot for Triangular Machine Translation—0
Improving Controllable Text Generation with Position-Aware Weighted Decoding—0
Compressing Sentence Representation via Homomorphic Projective Distillation—0
Unsupervised Dependency Graph Network—0
Your fairness may vary: Pretrained language model fairness in toxic text classification—0
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning—0
XLM-E: Cross-lingual Language Model Pre-training via ELECTRACode0
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data—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