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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 37513800 of 17610 papers

TitleStatusHype
BERT Goes Shopping: Comparing Distributional Models for Product RepresentationsCode1
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language ModelsCode1
Textually Pretrained Speech Language ModelsCode1
ThaiLMCut: Unsupervised Pretraining for Thai Word SegmentationCode1
Efficient Content-Based Sparse Attention with Routing TransformersCode1
The advantages of context specific language models: the case of the Erasmian Language ModelCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
The Benefits of Bad Advice: Autocontrastive Decoding across Model LayersCode1
The birth of Romanian BERTCode1
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
Accelerating Vision-Language Pretraining with Free Language ModelingCode1
The Compressor-Retriever Architecture for Language Model OSCode1
CriticEval: Evaluating Large Language Model as CriticCode1
The Devil in Linear TransformerCode1
Efficient Hierarchical Domain Adaptation for Pretrained Language ModelsCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Efficient Neural Architecture Search via Parameter SharingCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
The FineWeb Datasets: Decanting the Web for the Finest Text Data at ScaleCode1
Effective Sequence-to-Sequence Dialogue State TrackingCode1
The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False DatasetsCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event ExtractionCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Effective Attention Sheds Light On InterpretabilityCode1
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and InterpretabilityCode1
CDLM: Cross-Document Language ModelingCode1
AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZCode1
Effective Batching for Recurrent Neural Network GrammarsCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
The Radiation Oncology NLP DatabaseCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
Efficient OCR for Building a Diverse Digital HistoryCode1
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music RetrievalCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
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
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question AnsweringCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity DatasetCode1
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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