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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 38013850 of 17610 papers

TitleStatusHype
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language ModelingCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Toeplitz Neural Network for Sequence ModelingCode1
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
Efficient Nearest Neighbor Language ModelsCode1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
TopoLM: brain-like spatio-functional organization in a topographic language modelCode1
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language ModelCode1
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-TrainingCode1
Effective Batching for Recurrent Neural Network GrammarsCode1
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence LearningCode1
Analysing Lexical Semantic Change with Contextualised Word RepresentationsCode1
Effective Attention Sheds Light On InterpretabilityCode1
Towards autonomous system: flexible modular production system enhanced with large language model agentsCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
BEND: Benchmarking DNA Language Models on biologically meaningful tasksCode1
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning ApproachCode1
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured LearningCode1
Autonomous Microscopy Experiments through Large Language Model AgentsCode1
Addressing Some Limitations of Transformers with Feedback MemoryCode1
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity DatasetCode1
CTRL: A Conditional Transformer Language Model for Controllable GenerationCode1
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
Towards Open-World Text-Guided Face Image Generation and ManipulationCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
Towards Real-World Adverse Weather Image Restoration: Enhancing Clearness and Semantics with Vision-Language ModelsCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt LearningCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
C-STS: Conditional Semantic Textual SimilarityCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language CorrectionsCode1
Distilling a Pretrained Language Model to a Multilingual ASR ModelCode1
TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation SafetyCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
Trained on 100 million words and still in shape: BERT meets British National CorpusCode1
Cross-Thought for Sentence Encoder Pre-trainingCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training DataCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
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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