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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 75017550 of 17610 papers

TitleStatusHype
StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction0
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM0
Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation0
Storyboard guided Alignment for Fine-grained Video Action Recognition0
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool0
Story Cloze Task: UW NLP System0
Strategic Data Ordering: Enhancing Large Language Model Performance through Curriculum Learning0
Strategic Insights in Human and Large Language Model Tactics at Word Guessing Games0
Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation0
StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant0
Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models0
StreamLink: Large-Language-Model Driven Distributed Data Engineering System0
StreamVoice+: Evolving into End-to-end Streaming Zero-shot Voice Conversion0
StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion0
StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model0
Strengthening Fake News Detection: Leveraging SVM and Sophisticated Text Vectorization Techniques. Defying BERT?0
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding0
StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond0
StructFormer: Document Structure-based Masked Attention and its Impact on Language Model Pre-Training0
Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization0
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game0
Structural Embedding Projection for Contextual Large Language Model Inference0
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification0
Structural Language Models for Any-Code Generation0
Structural Realization with GGNNs0
Structural Reformation of Large Language Model Neuron Encapsulation for Divergent Information Aggregation0
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies0
Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance0
Structure-Aware Corpus Construction and User-Perception-Aligned Metrics for Large-Language-Model Code Completion0
Structured Agent Distillation for Large Language Model0
Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations0
Structured Convergence in Large Language Model Representations via Hierarchical Latent Space Folding0
Structured information extraction from complex scientific text with fine-tuned large language models0
Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training0
Structured Knowledge Discovery from Massive Text Corpus0
Structured Object Language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising0
Structured Penalties for Log-Linear Language Models0
Structured Pruning of Recurrent Neural Networks through Neuron Selection0
Structured Sparsification of Gated Recurrent Neural Networks0
Structure Guided Large Language Model for SQL Generation0
Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text0
Structure Language Models for Protein Conformation Generation0
Structure Learning in Weighted Languages0
STT: Soft Template Tuning for Few-Shot Learning0
STT: Soft Template Tuning for Few-Shot Adaptation0
Student as an Inherent Denoiser of Noisy Teacher0
Studying frequency-based approaches to process lexical simplification (Approches \`a base de fr\'equences pour la simplification lexicale) [in French]0
Studying Strategically: Learning to Mask for Closed-book QA0
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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