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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 27012750 of 17610 papers

TitleStatusHype
CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl DataCode1
CC-Riddle: A Question Answering Dataset of Chinese Character RiddlesCode1
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual ImagesCode1
Language Modeling with Editable External KnowledgeCode1
Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender SystemsCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Knowledge-Grounded Dialogue Generation with Pre-trained Language ModelsCode1
Knowledge Graph-Driven Retrieval-Augmented Generation: Integrating Deepseek-R1 with Weaviate for Advanced Chatbot ApplicationsCode1
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysisCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
CrAM: A Compression-Aware MinimizerCode1
Knowledge Graph Generation From TextCode1
Knowledge Perceived Multi-modal Pretraining in E-commerceCode1
Knowledge Enhanced Masked Language Model for Stance DetectionCode1
Coherence boosting: When your pretrained language model is not paying enough attentionCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
Knowledge-enhanced Visual-Language Pretraining for Computational PathologyCode1
CFBenchmark: Chinese Financial Assistant Benchmark for Large Language ModelCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
CFGPT: Chinese Financial Assistant with Large Language ModelCode1
Boosted Prompt Ensembles for Large Language ModelsCode1
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent ClassificationCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question AnsweringCode1
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge AcquisitionCode1
Accurate Retraining-free Pruning for Pretrained Encoder-based Language ModelsCode1
Chain of Images for Intuitively ReasoningCode1
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous SourcesCode1
Effective Batching for Recurrent Neural Network GrammarsCode1
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant SupervisionCode1
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded HallucinationsCode1
BOLT: Boost Large Vision-Language Model Without Training for Long-form Video UnderstandingCode1
Knowledge-Augmented Language Model VerificationCode1
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text ClassificationCode1
Counterfactual Token Generation in Large Language ModelsCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event ExtractionCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial ApplicationCode1
Enhancing Multilingual Language Model with Massive Multilingual Knowledge TriplesCode1
An Open Source Data Contamination Report for Large Language ModelsCode1
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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