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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1630116350 of 17610 papers

TitleStatusHype
Document Informed Neural Autoregressive Topic Models with Distributional PriorCode0
A Tool for Generating Exceptional Behavior Tests With Large Language ModelsCode0
Autoregressive Pre-Training on Pixels and TextsCode0
DoCIA: An Online Document-Level Context Incorporation Agent for Speech TranslationCode0
A Tool for Facilitating OCR Postediting in Historical DocumentsCode0
Doc2Dict: Information Extraction as Text GenerationCode0
Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language ModelingCode0
DnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text GenerationCode0
INSPECT: Intrinsic and Systematic Probing Evaluation for Code TransformersCode0
DNAZEN: Enhanced Gene Sequence Representations via Mixed Granularities of Coding UnitsCode0
CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural TextCode0
CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for Point Cloud Quality AssessmentCode0
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation FrameworkCode0
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented GeneratorCode0
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot ApproachCode0
DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare DiseasesCode0
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domainCode0
DNAHLM -- DNA sequence and Human Language mixed large language ModelCode0
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language ModelCode0
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text GenerationCode0
Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative WritingCode0
Clinical Flair: A Pre-Trained Language Model for Spanish Clinical Natural Language ProcessingCode0
Dwell in the Beginning: How Language Models Embed Long Documents for Dense RetrievalCode0
A Theoretically Grounded Application of Dropout in Recurrent Neural NetworksCode0
DynaBERT: Dynamic BERT with Adaptive Width and DepthCode0
IPAD: Iterative, Parallel, and Diffusion-based Network for Scene Text RecognitionCode0
A Targeted Assessment of Incremental Processing in Neural LanguageModels and HumansCode0
ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMsCode0
FinBERT: Financial Sentiment Analysis with Pre-trained Language ModelsCode0
Improving the Efficiency of Visually Augmented Language ModelsCode0
FiNCAT: Financial Numeral Claim Analysis ToolCode0
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution DistortionCode0
Grounding of Textual Phrases in Images by ReconstructionCode0
Diversity Measures: Domain-Independent Proxies for Failure in Language Model QueriesCode0
Distributionally robust self-supervised learning for tabular dataCode0
Dynamic Demonstrations Controller for In-Context LearningCode0
Dynamic Entity Representations in Neural Language ModelsCode0
Dynamic Evaluation of Neural Sequence ModelsCode0
Dynamic Evaluation of Transformer Language ModelsCode0
Table2Vec: Neural Word and Entity Embeddings for Table Population and RetrievalCode0
Finding Function in Form: Compositional Character Models for Open Vocabulary Word RepresentationCode0
Distributionally Robust Language ModelingCode0
Finding Hierarchical Structure in Neural Stacks Using Unsupervised ParsingCode0
A Tailored Pre-Training Model for Task-Oriented Dialog GenerationCode0
Instance Regularization for Discriminative Language Model Pre-trainingCode0
Group and Shuffle: Efficient Structured Orthogonal ParametrizationCode0
Distributional Discrepancy: A Metric for Unconditional Text GenerationCode0
iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language modelsCode0
AMuRD: Annotated Arabic-English Receipt Dataset for Key Information Extraction and ClassificationCode0
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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