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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 34013450 of 17610 papers

TitleStatusHype
Conformal Language ModelingCode1
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence InformationCode1
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
Q-learning with Language Model for Edit-based Unsupervised SummarizationCode1
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target AtomsCode1
Elephants Never Forget: Testing Language Models for Memorization of Tabular DataCode1
Efficient Pre-training of Masked Language Model via Concept-based Curriculum MaskingCode1
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference OptimizationCode1
Quark: Controllable Text Generation with Reinforced UnlearningCode1
Adversarial Training for Aspect-Based Sentiment Analysis with BERTCode1
Efficient Online Data Mixing For Language Model Pre-TrainingCode1
Query Performance Prediction using Relevance Judgments Generated by Large Language ModelsCode1
Questions Are All You Need to Train a Dense Passage RetrieverCode1
Efficient Nearest Neighbor Language ModelsCode1
Efficient Neural Architecture Search via Parameter SharingCode1
Efficient Long Sequence Modeling via State Space Augmented TransformerCode1
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Efficient OCR for Building a Diverse Digital HistoryCode1
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive PruningCode1
ELI5: Long Form Question AnsweringCode1
Efficient Content-Based Sparse Attention with Routing TransformersCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Log Parsing: How Far Can ChatGPT Go?Code1
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation LearningCode1
AuditWen:An Open-Source Large Language Model for AuditCode1
Effective Sequence-to-Sequence Dialogue State TrackingCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event ExtractionCode1
Efficient Hierarchical Domain Adaptation for Pretrained Language ModelsCode1
RDF2Vec: RDF Graph Embeddings and Their ApplicationsCode1
RDRec: Rationale Distillation for LLM-based RecommendationCode1
Adaptive Input Representations for Neural Language ModelingCode1
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich Translation in a Constructed LanguageCode1
READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language ModelingCode1
RealCritic: Towards Effectiveness-Driven Evaluation of Language Model CritiquesCode1
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMsCode1
Real-time Animation Generation and Control on Rigged Models via Large Language ModelsCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
Contextualized Perturbation for Textual Adversarial AttackCode1
Effective Attention Sheds Light On InterpretabilityCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language ModelCode1
Show:102550
← PrevPage 69 of 353Next →

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