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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9701–9750 of 17610 papers

TitleStatusHype
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification—0
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the WildCode0
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning—0
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
SeTformer is What You Need for Vision and Language—0
Maintaining Journalistic Integrity in the Digital Age: A Comprehensive NLP Framework for Evaluating Online News Content—0
Long Context Compression with Activation Beacon—0
LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward—0
CharPoet: A Chinese Classical Poetry Generation System Based on Token-free LLM—0
Part-of-Speech Tagger for Bodo Language using Deep Learning approach—0
PIXAR: Auto-Regressive Language Modeling in Pixel Space—0
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling—0
Enhancing Context Through Contrast—0
Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task—0
DocGraphLM: Documental Graph Language Model for Information Extraction—0
Thousands of AI Authors on the Future of AI—0
Object-Centric Instruction Augmentation for Robotic Manipulation—0
On the Stability of a non-hyperbolic nonlinear map with non-bounded set of non-isolated fixed points with applications to Machine LearningCode0
XUAT-Copilot: Multi-Agent Collaborative System for Automated User Acceptance Testing with Large Language Model—0
VoroNav: Voronoi-based Zero-shot Object Navigation with Large Language Model—0
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology ImagingCode0
AraCovTexFinder: Leveraging the transformer-based language model for Arabic COVID-19 text identificationCode0
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models—0
PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model—0
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment—0
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives—0
Memory, Consciousness and Large Language Model—0
Understanding LLMs: A Comprehensive Overview from Training to Inference—0
Large Language Model Capabilities in Perioperative Risk Prediction and PrognosticationCode0
Predicting challenge moments from students' discourse: A comparison of GPT-4 to two traditional natural language processing approaches—0
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition—0
Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling—0
Cross-target Stance Detection by Exploiting Target Analytical Perspectives—0
BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving—0
Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control—0
Discovering Significant Topics from Legal Decisions with Selective Inference—0
Efficient Parallel Audio Generation using Group Masked Language Modeling—0
Cheetah: Natural Language Generation for 517 African LanguagesCode0
DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever—0
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training—0
General Point Model Pretraining with Autoencoding and AutoregressiveCode0
AssistGUI: Task-Oriented PC Graphical User Interface Automation—0
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training—0
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior—0
Few-Shot Object Detection with Foundation Models—0
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation—0
Discovering Syntactic Interaction Clues for Human-Object Interaction Detection—0
Disentangled Prompt Representation for Domain Generalization—0
Pixel-Aligned Language Model—0
Jack of All Tasks Master of Many: Designing General-Purpose Coarse-to-Fine Vision-Language Model—0
Show:102550
← PrevPage 195 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