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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9401–9425 of 17610 papers

TitleStatusHype
Optimizing the Design of an Artificial Pancreas to Improve Diabetes Management—0
LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes—0
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation—0
NLP for Knowledge Discovery and Information Extraction from Energetics Corpora—0
Sentinels of the Stream: Unleashing Large Language Models for Dynamic Packet Classification in Software Defined Networks -- Position Paper—0
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought—0
A Tale of Tails: Model Collapse as a Change of Scaling Laws—0
History, Development, and Principles of Large Language Models-An Introductory Survey—0
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation—0
G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in GermanCode0
GS-CLIP: Gaussian Splatting for Contrastive Language-Image-3D Pretraining from Real-World Data—0
A self-supervised framework for learning whole slide representations—0
Large Language Models for Captioning and Retrieving Remote Sensing Images—0
Self-consistent context aware conformer transducer for speech recognition—0
Language Model Sentence Completion with a Parser-Driven Rhetorical Control MethodCode0
Task Supportive and Personalized Human-Large Language Model Interaction: A User Study—0
LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education—0
Large Language Model Meets Graph Neural Network in Knowledge Distillation—0
Neural Models for Source Code Synthesis and Completion—0
Text-to-Code Generation with Modality-relative Pre-training—0
Question Aware Vision Transformer for Multimodal Reasoning—0
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks—0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
Large Language Model Augmented Exercise Retrieval for Personalized Language Learning—0
How do Transformers perform In-Context Autoregressive Learning?—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