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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10401–10450 of 17610 papers

TitleStatusHype
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning—0
Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model—0
Preference Alignment Improves Language Model-Based TTS—0
Preference optimization of protein language models as a multi-objective binder design paradigm—0
Preferential Normalizing Flows—0
PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization—0
Prefix Embeddings for In-context Machine Translation—0
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization—0
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model—0
Preservation of Recognizability for Weighted Linear Extended Top-Down Tree Transducers—0
Preserving Distributional Information in Dialogue Act Classification—0
Two-stage LLM Fine-tuning with Less Specialization and More Generalization—0
Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression—0
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
Pre-trained Language Model Based Active Learning for Sentence Matching—0
Pre-trained Language Model based Ranking in Baidu Search—0
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction—0
Pre-trained Language Model for Web-scale Retrieval in Baidu Search—0
Pretrained Language Model in Continual Learning: A Comparative Study—0
Pre-trained Language Model Representations for Language Generation—0
Pretrained Language Models Are All You Need For Text-to-SQL Schema Linking—0
Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation—0
Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts—0
Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing—0
Pre-training with Meta Learning for Chinese Word Segmentation—0
Pre-trained protein language model for codon optimization—0
Vision-Language Model Selection and Reuse for Downstream Adaptation—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—0
Pre-Training a Language Model Without Human Language—0
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs—0
Pretraining and Updates of Domain-Specific LLM: A Case Study in the Japanese Business Domain—0
Pre-Training BERT on Domain Resources for Short Answer Grading—0
Pretraining Chinese BERT for Detecting Word Insertion and Deletion Errors—0
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model—0
Krutrim LLM: A Novel Tokenization Strategy for Multilingual Indic Languages with Petabyte-Scale Data Processing—0
Pre-training data selection for biomedical domain adaptation using journal impact metrics—0
Pre-Training for Query Rewriting in A Spoken Language Understanding System—0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning—0
Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning—0
Pre-training Language Model as a Multi-perspective Course Learner—0
Pretraining Large Brain Language Model for Active BCI: Silent Speech—0
Pretraining Sentiment Classifiers with Unlabeled Dialog Data—0
Pre-training Text Representations as Meta Learning—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data—0
Pre-training with Large Language Model-based Document Expansion for Dense Passage Retrieval—0
Pre-Training With Scientific Text Improves Educational Question Generation—0
Pretrain Knowledge-Aware Language Models—0
Show:102550
← PrevPage 209 of 353Next →
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