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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 48514900 of 17610 papers

TitleStatusHype
CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language ModelsCode0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
Developing Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models?Code0
MiniDisc: Minimal Distillation Schedule for Language Model CompressionCode0
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural NetworksCode0
GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific NarrativesCode0
Autoencoders as Tools for Program SynthesisCode0
AnnoDPO: Protein Functional Annotation Learning with Direct Preference OptimizationCode0
Chaining thoughts and LLMs to learn DNA structural biophysicsCode0
Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional SemanticsCode0
Chain of Code: Reasoning with a Language Model-Augmented Code EmulatorCode0
GPT-based Generation for Classical Chinese PoetryCode0
Device Placement Optimization with Reinforcement LearningCode0
Autoencoding Undirected Molecular Graphs With Neural NetworksCode0
Chain-of-Model Learning for Language ModelCode0
Diagnosing our datasets: How does my language model learn clinical information?Code0
Abstractive Text Summarization based on Language Model Conditioning and Locality ModelingCode0
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative ModelsCode0
Tracr-Injection: Distilling Algorithms into Pre-trained Language ModelsCode0
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LMCode0
Gradient-based learning applied to document recognitionCode0
Gradual Learning of Recurrent Neural NetworksCode0
Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution PipelineCode0
GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language ModelCode0
Grammar Induction with Neural Language Models: An Unusual ReplicationCode0
A Non-monotonic Self-terminating Language ModelCode0
AUTOMATED AUDIO CAPTIONING BY FINE-TUNING BART WITH AUDIOSET TAGSCode0
Challenges in Measuring Bias via Open-Ended Language GenerationCode0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
Graph-based Uncertainty Metrics for Long-form Language Model OutputsCode0
Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and HealthCode0
DIBERT: Dependency Injected Bidirectional Encoder Representations from TransformersCode0
A Note on Learning Rare Events in Molecular Dynamics using LSTM and TransformerCode0
Graphemic Normalization of the Perso-Arabic ScriptCode0
ChamaleonLLM: Batch-Aware Dynamic Low-Rank Adaptation via Inference-Time ClustersCode0
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language ModelsCode0
Dict-BERT: Enhancing Language Model Pre-training with DictionaryCode0
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and FinetuningCode0
Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncodersCode0
A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language ModelsCode0
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary PatternsCode0
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and BiasCode0
GraphSeqLM: A Unified Graph Language Framework for Omic Graph LearningCode0
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation ExtractionCode0
Differentiable N-gram Objective on Abstractive SummarizationCode0
Grid Long Short-Term MemoryCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Differentiable Random Access Memory using LatticesCode0
Automated Evaluation of Out-of-Context ErrorsCode0
A Controlled Reevaluation of Coreference Resolution ModelsCode0
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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