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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 33013350 of 17610 papers

TitleStatusHype
Aligning LLM Agents by Learning Latent Preference from User EditsCode1
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language ModelsCode1
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt VerbalizerCode1
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News ArticlesCode1
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question AnsweringCode1
BiasEdit: Debiasing Stereotyped Language Models via Model EditingCode1
EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence InformationCode1
CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in ConversationCode1
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-ThoughtCode1
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive PruningCode1
Efficient Pre-training of Masked Language Model via Concept-based Curriculum MaskingCode1
Composable Text Controls in Latent Space with ODEsCode1
Efficient Online Data Mixing For Language Model Pre-TrainingCode1
Efficient OCR for Building a Diverse Digital HistoryCode1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Composing Parameter-Efficient Modules with Arithmetic OperationsCode1
An Explanation of In-context Learning as Implicit Bayesian InferenceCode1
Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token PredictionCode1
PLLaMa: An Open-source Large Language Model for Plant ScienceCode1
Compositional Chain-of-Thought Prompting for Large Multimodal ModelsCode1
Compositional Demographic Word EmbeddingsCode1
Efficient Long Sequence Modeling via State Space Augmented TransformerCode1
Efficient Nearest Neighbor Language ModelsCode1
Plug-and-Play Policy Planner for Large Language Model Powered Dialogue AgentsCode1
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
Efficient Hierarchical Domain Adaptation for Pretrained Language ModelsCode1
Learning Video Context as Interleaved Multimodal SequencesCode1
Compositional Morphology for Word Representations and Language ModellingCode1
ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language ModelCode1
Composition based oxidation state prediction of materials using deep learningCode1
Attention-based Contextual Language Model Adaptation for Speech RecognitionCode1
Compressed Context Memory For Online Language Model InteractionCode1
Efficient Content-Based Sparse Attention with Routing TransformersCode1
Efficient Neural Architecture Search via Parameter SharingCode1
Elastic Weight Removal for Faithful and Abstractive Dialogue GenerationCode1
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding MatchingCode1
Beyond Size: How Gradients Shape Pruning Decisions in Large Language ModelsCode1
On Large Language Model Continual UnlearningCode1
Effective Sequence-to-Sequence Dialogue State TrackingCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language ModelCode1
Compressive Transformers for Long-Range Sequence ModellingCode1
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term ConversationsCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event ExtractionCode1
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information ExtractionCode1
Effective Batching for Recurrent Neural Network GrammarsCode1
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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