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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1550115550 of 17610 papers

TitleStatusHype
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual RetrievalCode0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
Towards Democratized Flood Risk Management: An Advanced AI Assistant Enabled by GPT-4 for Enhanced Interpretability and Public EngagementCode0
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense ReasoningCode0
LaMemo: Language Modeling with Look-Ahead MemoryCode0
Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian LawsCode0
KGLink: A column type annotation method that combines knowledge graph and pre-trained language modelCode0
Self Supervision for Attention NetworksCode0
Panoramic Interests: Stylistic-Content Aware Personalized Headline GenerationCode0
Self-Train Before You TranscribeCode0
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant AnnotationsCode0
Mistral-SPLADE: LLMs for better Learned Sparse RetrievalCode0
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
Self-training Large Language Models through Knowledge DetectionCode0
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence LabelingCode0
Misinformation Has High PerplexityCode0
Learning Private Neural Language Modeling with Attentive AggregationCode0
Language Models Still Struggle to Zero-shot Reason about Time SeriesCode0
Tree Transformer: Integrating Tree Structures into Self-AttentionCode0
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language ModelCode0
Semantically Grounded Object Matching for Robust Robotic Scene RearrangementCode0
Semantically Meaningful Metrics for Norwegian ASR SystemsCode0
Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language frameworkCode0
Juman++: A Morphological Analysis Toolkit for Scriptio ContinuaCode0
Learning Parametric Distributions from Samples and PreferencesCode0
PanGu-Coder: Program Synthesis with Function-Level Language ModelingCode0
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language ModelCode0
SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language ModelCode0
Trellis Networks for Sequence ModelingCode0
Semantic Coherence Markers for the Early Diagnosis of the Alzheimer DiseaseCode0
Language models show human-like content effects on reasoning tasksCode0
Using Persuasive Writing Strategies to Explain and Detect Health MisinformationCode0
MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context UnderstandingCode0
Minimizing PLM-Based Few-Shot Intent DetectorsCode0
SweCTRL-Mini: a data-transparent Transformer-based large language model for controllable text generation in SwedishCode0
PaLM: A Hybrid Parser and Language ModelCode0
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&ACode0
Mini Minds: Exploring Bebeshka and Zlata Baby ModelsCode0
Semantic Labeling Using a Deep Contextualized Language ModelCode0
LAMOL: LAnguage MOdeling for Lifelong Language LearningCode0
Mind Scramble: Unveiling Large Language Model Psychology Via TypoglycemiaCode0
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary AlgorithmsCode0
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
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context LearningCode0
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Planning with Multi-Constraints via Collaborative Language AgentsCode0
MetaSC: Test-Time Safety Specification Optimization for Language ModelsCode0
Language Model Sentence Completion with a Parser-Driven Rhetorical Control MethodCode0
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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