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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 9051–9075 of 17610 papers

TitleStatusHype
Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing PlatformCode0
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization—0
Learning to Describe for Predicting Zero-shot Drug-Drug InteractionsCode0
Masked Generative Story Transformer with Character Guidance and Caption AugmentationCode0
Simple and Scalable Strategies to Continually Pre-train Large Language Models—0
Boosting Disfluency Detection with Large Language Model as Disfluency GeneratorCode0
Is Context Helpful for Chat Translation Evaluation?—0
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback—0
Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs—0
Do Large Language Models Solve ARC Visual Analogies Like People Do?Code0
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship ClassificationCode0
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents—0
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking—0
BAGEL: Bootstrapping Agents by Guiding Exploration with Language—0
CKERC : Joint Large Language Models with Commonsense Knowledge for Emotion Recognition in Conversation—0
Efficient Language Model Architectures for Differentially Private Federated Learning—0
generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation—0
TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection—0
Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration—0
Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningCode0
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model—0
Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization—0
Knowledge Graph Large Language Model (KG-LLM) for Link Prediction—0
The future of document indexing: GPT and Donut revolutionize table of content processing—0
Towards Graph Foundation Models for Personalization—0
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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