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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 43514400 of 17610 papers

TitleStatusHype
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument CompatibilitiesCode0
AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and GenerationCode0
Exploring Graph Representations of Logical Forms for Language ModelingCode0
Block-wise Dynamic SparsenessCode0
Exploring Language Model Generalization in Low-Resource Extractive QACode0
Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal DocumentsCode0
Blockwise Self-Attention for Long Document UnderstandingCode0
Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese HokkienCode0
Exploring Multilingual Text Data DistillationCode0
Correcting misinformation on social media with a large language modelCode0
Exploring Multitask Learning for Low-Resource Abstractive SummarizationCode0
A Statistical Investigation of Long Memory in Language and MusicCode0
Anchor Points: Benchmarking Models with Much Fewer ExamplesCode0
A statistical significance testing approach for measuring term burstiness with applications to domain-specific terminology extractionCode0
Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep HealthCode0
Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language ModelingCode0
Adaptively Truncating Backpropagation Through Time to Control Gradient BiasCode0
BoK: Introducing Bag-of-Keywords Loss for Interpretable Dialogue Response GenerationCode0
Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic SimilarityCode0
CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven AgentsCode0
Co-STAR: Collaborative Curriculum Self-Training with Adaptive Regularization for Source-Free Video Domain AdaptationCode0
Scaling Trends in Language Model RobustnessCode0
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language ModelsCode0
ASTPrompter: Weakly Supervised Automated Language Model Red-Teaming to Identify Low-Perplexity Toxic PromptsCode0
Counterfactual Language Model Adaptation for Suggesting PhrasesCode0
Counterfactually Probing Language Identity in Multilingual ModelsCode0
AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length ControlCode0
Exploring the Design Space of Visual Context Representation in Video MLLMsCode0
Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankersCode0
Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language ModelsCode0
Exploring the Landscape for Generative Sequence Models for Specialized Data SynthesisCode0
Boosting Disfluency Detection with Large Language Model as Disfluency GeneratorCode0
An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue AssistantCode0
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language ModelsCode0
Is attention required for ICL? Exploring the Relationship Between Model Architecture and In-Context Learning AbilityCode0
Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial AnalysisCode0
A Common Pitfall of Margin-based Language Model Alignment: Gradient EntanglementCode0
Transformers on Multilingual Clause-Level MorphologyCode0
Exploring the Syntactic Abilities of RNNs with Multi-task LearningCode0
Boosting Large Language Models with Mask Fine-TuningCode0
A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News HeadlinesCode0
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment ClassificationCode0
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class ClassificationCode0
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
Exploring the zero-shot limit of FewRelCode0
Exploring Transformer ExtrapolationCode0
Exploring Unsupervised Pretraining Objectives for Machine TranslationCode0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
COVID-19 Vaccine Misinformation in Middle Income CountriesCode0
Exploring Weight Symmetry in Deep Neural NetworksCode0
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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