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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1680116850 of 17610 papers

TitleStatusHype
Advancing State of the Art in Language ModelingCode0
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL VanishingCode0
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets -- RoBERTa Ensembles and The Continued Relevance of Handcrafted FeaturesCode0
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of TransformersCode0
Accessible Smart Contracts Verification: Synthesizing Formal Models with Tamed LLMsCode0
ALMANACS: A Simulatability Benchmark for Language Model ExplainabilityCode0
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text GenerationCode0
ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA ObjectiveCode0
Enhancing Content-based Recommendation via Large Language ModelCode0
Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement LearningCode0
Customising General Large Language Models for Specialised Emotion Recognition TasksCode0
Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task LearningCode0
AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain RecommendationsCode0
Advancing Regular Language Reasoning in Linear Recurrent Neural NetworksCode0
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual TemplatesCode0
cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSECode0
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language ModelsCode0
Curriculum learning for language modelingCode0
A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative GenerationCode0
Improved training of neural trans-dimensional random field language models with dynamic noise-contrastive estimationCode0
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change AnalysisCode0
CURIE: An Iterative Querying Approach for Reasoning About SituationsCode0
Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairsCode0
Enhancing Domain Word Embedding via Latent Semantic ImputationCode0
Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-TuningCode0
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial DesignCode0
Enhancing elusive clues in knowledge learning by contrasting attention of language modelsCode0
Accelerating Training of Transformer-Based Language Models with Progressive Layer DroppingCode0
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
Integrating Large Language Models in Causal Discovery: A Statistical Causal ApproachCode0
Can ChatGPT's Responses Boost Traditional Natural Language Processing?Code0
Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System ResponsesCode0
A Systematic Comparison of Architectures for Document-Level Sentiment ClassificationCode0
Hierarchical Multiscale Recurrent Neural NetworksCode0
A Comparative Study on Language Models for Task-Oriented Dialogue SystemsCode0
From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue AgentsCode0
Enhancing Image Generation Fidelity via Progressive PromptsCode0
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual UnderstandingCode0
Improved Word Representation Learning with SememesCode0
Can a Large Language Model Learn Matrix Functions In Context?Code0
Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded DialogueCode0
Disentangling Language and Knowledge in Task-Oriented DialogsCode0
Enhancing Language Model Factuality via Activation-Based Confidence Calibration and Guided DecodingCode0
Arabic Synonym BERT-based Adversarial Examples for Text ClassificationCode0
Hierarchical Quantized Representations for Script GenerationCode0
Enhancing Natural Language Representation with Large-Scale Out-of-Domain CommonsenseCode0
Improve Language Model and Brain Alignment via Associative MemoryCode0
Integrating Linguistic Theory and Neural Language ModelsCode0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Iterative Counterfactual Data AugmentationCode0
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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