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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 37513775 of 17610 papers

TitleStatusHype
ELMS: Elasticized Large Language Models On Mobile Devices0
A Hetero-functional Graph Resilience Analysis for Convergent Systems-of-Systems0
Interactive Machine Teaching by Labeling Rules and Instances0
STLLM-DF: A Spatial-Temporal Large Language Model with Diffusion for Enhanced Multi-Mode Traffic System Forecasting0
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?Code0
MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality0
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery0
Achieving Peak Performance for Large Language Models: A Systematic Review0
POINTS: Improving Your Vision-language Model with Affordable Strategies0
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support0
How Does Code Pretraining Affect Language Model Task Performance?0
Sparse Rewards Can Self-Train Dialogue AgentsCode1
Customizing Large Language Model Generation Style using Parameter-Efficient Finetuning0
Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study0
Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets0
GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding0
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
Multi-Programming Language Ensemble for Code Generation in Large Language ModelCode0
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment ClassificationCode0
N-gram Prediction and Word Difference Representations for Language Modeling0
LAST: Language Model Aware Speech Tokenization0
The AdEMAMix Optimizer: Better, Faster, OlderCode2
Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers0
A Fused Large Language Model for Predicting Startup Success0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified