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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 43014350 of 17610 papers

TitleStatusHype
Expansion via Prediction of Importance with ContextualizationCode0
Controlled Text Generation for Black-box Language Models via Score-based Progressive EditorCode0
Biomedical Event Extraction as Multi-turn Question AnsweringCode0
Biomedical Language Models are Robust to Sub-optimal TokenizationCode0
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence MaskingCode0
Assessing the Promise and Pitfalls of ChatGPT for Automated Code GenerationCode0
Explainable and Discourse Topic-aware Neural Language UnderstandingCode0
Controlling Large Language Model with Latent ActionsCode0
Controlling the Amount of Verbatim Copying in Abstractive SummarizationCode0
Controlling the Imprint of Passivization and Negation in Contextualized RepresentationsCode0
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional ReasoningCode0
BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity RecognitionCode0
Explaining Context Length Scaling and Bounds for Language ModelsCode0
Explaining Natural Language Processing Classifiers with Occlusion and Language ModelingCode0
Converge to the Truth: Factual Error Correction via Iterative Constrained EditingCode0
Conversational AI Powered by Large Language Models Amplifies False Memories in Witness InterviewsCode0
BIRCO: A Benchmark of Information Retrieval Tasks with Complex ObjectivesCode0
Explanation Graph Generation via Generative Pre-training over Synthetic GraphsCode0
Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative AnalysisCode0
Explanation Regeneration via Information BottleneckCode0
Assessing the Reliability of Large Language Model KnowledgeCode0
An Approach for Text Steganography Based on Markov ChainsCode0
Explicit Sparse Transformer: Concentrated Attention Through Explicit SelectionCode0
Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct FeaturesCode0
Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple LevelsCode0
ViTHSD: Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media TextsCode0
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal SummarizationCode0
Conversations in Galician: a Large Language Model for an Underrepresented LanguageCode0
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages StudyCode0
Black-box language model explanation by context length probingCode0
A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown DetectionCode0
Converting the Point of View of Messages Spoken to Virtual AssistantsCode0
Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detectionCode0
Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun ResolutionCode0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance ApproachCode0
Conveyor: Efficient Tool-aware LLM Serving with Tool Partial ExecutionCode0
Convolutional Neural Network for Paraphrase IdentificationCode0
Convolutional Neural Network Language ModelsCode0
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large VocabulariesCode0
Blank Collapse: Compressing CTC emission for the faster decodingCode0
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition ModelingCode0
Exploring and Verbalizing Academic Ideas by Concept Co-occurrenceCode0
Anatomy of Neural Language ModelsCode0
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language ModelsCode0
CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialoguesCode0
COrAL: Order-Agnostic Language Modeling for Efficient Iterative RefinementCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
StructEval: Deepen and Broaden Large Language Model Assessment via Structured EvaluationCode0
CORE: A Retrieve-then-Edit Framework for Counterfactual Data 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