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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 33513400 of 17610 papers

TitleStatusHype
Efficient OCR for Building a Diverse Digital HistoryCode1
Pretraining Language Models with Human PreferencesCode1
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross AttentionsCode1
Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language ModelsCode1
"Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced DistillationCode1
Pre-training Text-to-Text Transformers for Concept-centric Common SenseCode1
Efficient Long Sequence Modeling via State Space Augmented TransformerCode1
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text TranslationCode1
Adaptive Attention Span in Computer VisionCode1
Prioritized Semantic Learning for Zero-shot Instance NavigationCode1
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target AtomsCode1
Concept Bottleneck Large Language ModelsCode1
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word OrderCode1
Efficient Hierarchical Domain Adaptation for Pretrained Language ModelsCode1
Probabilistic Inference in Language Models via Twisted Sequential Monte CarloCode1
Probing Across Time: What Does RoBERTa Know and When?Code1
Efficient Online Data Mixing For Language Model Pre-TrainingCode1
Adaptive Attention Span in TransformersCode1
Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEditCode1
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text GenerationCode1
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
AttributionBench: How Hard is Automatic Attribution Evaluation?Code1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference OptimizationCode1
Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy PlanningCode1
Adversarial Training for Aspect-Based Sentiment Analysis with BERTCode1
PromptCap: Prompt-Guided Task-Aware Image CaptioningCode1
Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4Code1
Conditioned Text Generation with Transfer for Closed-Domain Dialogue SystemsCode1
PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency AdaptationCode1
Prompting as Probing: Using Language Models for Knowledge Base ConstructionCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event ExtractionCode1
Efficient Content-Based Sparse Attention with Routing TransformersCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Prompting Visual-Language Models for Dynamic Facial Expression RecognitionCode1
PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept LinkingCode1
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech RecognitionCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based SamplingCode1
Effective Sequence-to-Sequence Dialogue State TrackingCode1
Effective Human-AI Teams via Learned Natural Language Rules and OnboardingCode1
Context-aware Stand-alone Neural Spelling CorrectionCode1
Configurable Safety Tuning of Language Models with Synthetic Preference DataCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
CONFLARE: CONFormal LArge language model REtrievalCode1
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
ConfliBERT: A Language Model for Political ConflictCode1
ConfliBERT: A Pre-trained Language Model for Political Conflict and ViolenceCode1
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
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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