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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4551–4600 of 17610 papers

TitleStatusHype
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
Continuous diffusion for categorical data—0
Continuous Learning in a Hierarchical Multiscale Neural Network—0
Continuous multilinguality with language vectors—0
Continuous multilinguality with language vectors—0
Continuous Pseudo-Labeling from the Start—0
Continuous Space Translation Models with Neural Networks—0
Contractive error feedback for gradient compression—0
Contrasting Linguistic Patterns in Human and LLM-Generated News Text—0
Chain-of-Thought Augmentation with Logit Contrast for Enhanced Reasoning in Language Models—0
Contrastive Conditional Masked Language Model for Non-autoregressive Neural Machine Translation—0
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning—0
Contrastive Entropy: A new evaluation metric for unnormalized language models—0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems—0
Contrastive Learning for Low Resource Machine Translation—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
Contrastive Learning with Counterfactual Explanations for Radiology Report Generation—0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
Contri(e)ve: Context + Retrieve for Scholarly Question Answering—0
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation—0
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards—0
Controllable Natural Language Generation with Contrastive Prefixes—0
Controllable Natural Language Generation with Contrastive Prefixes—0
Controllable Response Generation for Assistive Use-cases—0
Controllable Text Generation in the Instruction-Tuning Era—0
Controllable Text Generation with Language Constraints—0
ControlVAE: Controllable Variational Autoencoder—0
Controlled Caption Generation for Images Through Adversarial Attacks—0
Controlled Cue Generation for Play Scripts—0
Controlled Decoding from Language Models—0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
Controlled Text Generation with Natural Language Instructions—0
Controlled Training Data Generation with Diffusion Models—0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
Controlling for Stereotypes in Multimodal Language Model Evaluation—0
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status—0
Controlling Large Language Model Agents with Entropic Activation Steering—0
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach—0
Controlling Linguistic Style Aspects in Neural Language Generation—0
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms—0
Controlling Translation Formality Using Pre-trained Multilingual Language Models—0
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation—0
Control Search Rankings, Control the World: What is a Good Search Engine?—0
Conversational Alignment with Artificial Intelligence in Context—0
Conversational Ontology Alignment with ChatGPT—0
Conversational Query Reformulation with the Guidance of Retrieved Documents—0
Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph—0
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition—0
Conversational Speech Recognition Needs Data? Experiments with Austrian German—0
Show:102550
← PrevPage 92 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