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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 40514100 of 17610 papers

TitleStatusHype
Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation RegularizationCode0
An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue AssistantCode0
Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLMCode0
Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation TrainingCode0
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion EnhancementCode0
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language ModelsCode0
Mitigating Test-Time Bias for Fair Image RetrievalCode0
DE-COP: Detecting Copyrighted Content in Language Models Training DataCode0
Using Persuasive Writing Strategies to Explain and Detect Health MisinformationCode0
Misinformation Has High PerplexityCode0
MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context UnderstandingCode0
Mitigating the Bias of Large Language Model EvaluationCode0
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response ForecastingCode0
BatchPrompt: Accomplish more with lessCode0
Anchor Points: Benchmarking Models with Much Fewer ExamplesCode0
I-AI: A Controllable & Interpretable AI System for Decoding Radiologists' Intense Focus for Accurate CXR DiagnosesCode0
Mini Minds: Exploring Bebeshka and Zlata Baby ModelsCode0
Mind Scramble: Unveiling Large Language Model Psychology Via TypoglycemiaCode0
Decoding fMRI Data into Captions using Prefix Language ModelingCode0
Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social MediaCode0
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
Mistral-SPLADE: LLMs for better Learned Sparse RetrievalCode0
Baseline: A Library for Rapid Modeling, Experimentation and Development of Deep Learning Algorithms targeting NLPCode0
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMsCode0
MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal OutputCode0
MILL: Mutual Verification with Large Language Models for Zero-Shot Query ExpansionCode0
Minimizing PLM-Based Few-Shot Intent DetectorsCode0
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context LearningCode0
A Dutch Financial Large Language ModelCode0
Planning with Multi-Constraints via Collaborative Language AgentsCode0
AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and GenerationCode0
Barack's Wife Hillary: Using Knowledge Graphs for Fact-Aware Language ModelingCode0
A Dual Encoder Sequence to Sequence Model for Open-Domain Dialogue ModelingCode0
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language ModelingCode0
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation LearningCode0
Meta-Learning the Difference: Preparing Large Language Models for Efficient AdaptationCode0
MetaSC: Test-Time Safety Specification Optimization for Language ModelsCode0
Debiasing Pre-Trained Language Models via Efficient Fine-TuningCode0
Meta-Context Transformers for Domain-Specific Response GenerationCode0
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment ClassificationCode0
Bangla Grammatical Error Detection Using T5 Transformer ModelCode0
Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model AgentCode0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
Meta Fine-Tuning Neural Language Models for Multi-Domain Text MiningCode0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
Anatomy of Neural Language ModelsCode0
Memory-Efficient Adaptive OptimizationCode0
Memory-efficient Stochastic methods for Memory-based TransformersCode0
Memory TransformerCode0
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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