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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 54015450 of 17610 papers

TitleStatusHype
Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case StudyCode0
Investigating the translation capabilities of Large Language Models trained on parallel data onlyCode0
Investigating Transferability in Pretrained Language ModelsCode0
Investigating variation in written forms of Nahuatl using character-based language modelsCode0
CoLMbo: Speaker Language Model for Descriptive ProfilingCode0
ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity RecognitionCode0
IPAD: Iterative, Parallel, and Diffusion-based Network for Scene Text RecognitionCode0
Elliptical AttentionCode0
IPOD: An Industrial and Professional Occupations Dataset and its Applications to Occupational Data Mining and AnalysisCode0
IPO: Your Language Model is Secretly a Preference ClassifierCode0
Colorless green recurrent networks dream hierarchicallyCode0
Multimodal Foundation Models Exploit Text to Make Medical Image PredictionsCode0
COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General PreferencesCode0
Combating Adversarial Attacks with Multi-Agent DebateCode0
Embedded Named Entity Recognition using Probing ClassifiersCode0
Combiner: Full Attention Transformer with Sparse Computation CostCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-AttentionCode0
Combining Analogy with Language Models for Knowledge ExtractionCode0
Embedding Hallucination for Few-Shot Language Fine-tuningCode0
Embedding Ontologies via Incorporating Extensional and Intensional KnowledgeCode0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
360^REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent SystemCode0
Is GPT-3 a Good Data Annotator?Code0
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language ModelsCode0
Is It Navajo? Accurate Language Detection in Endangered Athabaskan LanguagesCode0
Understanding and Robustifying Differentiable Architecture SearchCode0
Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidanceCode0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
Is Multilingual BERT Fluent in Language Generation?Code0
Emergence of a High-Dimensional Abstraction Phase in Language TransformersCode0
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task PlanningCode0
Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical InvestigationCode0
Emergent Linguistic Structures in Neural Networks are FragileCode0
Is Training Data Quality or Quantity More Impactful to Small Language Model Performance?Code0
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccinesCode0
Are Some Words Worth More than Others?Code0
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?Code0
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation SystemsCode0
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation SystemsCode0
EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language ModelsCode0
Item-side Fairness of Large Language Model-based Recommendation SystemCode0
Iterative Counterfactual Data AugmentationCode0
A Modular Approach for Multilingual Timex Detection and Normalization using Deep Learning and Grammar-based methodsCode0
EmoNews: A Spoken Dialogue System for Expressive News ConversationsCode0
Xmodel-2 Technical ReportCode0
uniblock: Scoring and Filtering Corpus with Unicode Block InformationCode0
UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource LanguagesCode0
UniDetox: Universal Detoxification of Large Language Models via Dataset DistillationCode0
Unified Language Model Pre-training for Natural Language Understanding and GenerationCode0
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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