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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6051–6100 of 17610 papers

TitleStatusHype
Answer Extraction by Recursive Parse Tree Descent—0
Answering real-world clinical questions using large language model based systems—0
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval—0
AntiBARTy Diffusion for Property Guided Antibody Design—0
Antibody Representation Learning for Drug Discovery—0
Anticipating Future with Large Language Model for Simultaneous Machine Translation—0
Anti-stereotypical Predictive Text Suggestions Do Not Reliably Yield Anti-stereotypical Writing—0
AntLM: Bridging Causal and Masked Language Models—0
An Unsupervised Parameter Estimation Algorithm for a Generative Dependency N-gram Language Model—0
An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora—0
An Unsupervised System for Parallel Corpus Filtering—0
An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation—0
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector—0
Any-Shift Prompting for Generalization over Distributions—0
Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation—0
AnyTOD: A Programmable Task-Oriented Dialog System—0
Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation—0
AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services—0
A PAC-Bayesian Approach to Minimum Perplexity Language Modeling—0
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags—0
A Partially Rule-Based Approach to AMR Generation—0
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking—0
A Performance Evaluation of a Quantized Large Language Model on Various Smartphones—0
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression—0
A Perspective on Literary Metaphor in the Context of Generative AI—0
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing—0
A Phrase Orientation Model for Hierarchical Machine Translation—0
A Physics-Inspired Optimizer: Velocity Regularized Adam—0
A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora—0
A Pilot Study on Dialogue-Level Dependency Parsing for Chinese—0
A Pipeline Approach to Supervised Error Correction for the QALB-2014 Shared Task—0
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model—0
Apolitical Intelligence? Auditing Delphi's responses on controversial political issues in the US—0
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning—0
A Post-editing Interface for Immediate Adaptation in Statistical Machine Translation—0
APo-VAE: Text Generation in Hyperbolic Space—0
Application-Agnostic Language Modeling for On-Device ASR—0
Application d'un algorithme de traduction statistique \`a la normalisation de textos (Applying a Statistical Machine Translation Algorithm to SMS Text Message Normalization) [in French]—0
Application of Multimodal Large Language Models in Autonomous Driving—0
Application of NotebookLM, a Large Language Model with Retrieval-Augmented Generation, for Lung Cancer Staging—0
Application of Quantum Tensor Networks for Protein Classification—0
Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving—0
Applications of Large Language Model Reasoning in Feature Generation—0
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model—0
Applications of Lexicographic Semirings to Problems in Speech and Language Processing—0
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation—0
Applying Collocation Segmentation to the ACL Anthology Reference Corpus—0
Applying Ensemble Methods to Model-Agnostic Machine-Generated Text Detection—0
Applying General Turn-taking Models to Conversational Human-Robot Interaction—0
Show:102550
← PrevPage 122 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