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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 53015350 of 17610 papers

TitleStatusHype
AraCovTexFinder: Leveraging the transformer-based language model for Arabic COVID-19 text identificationCode0
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language ModelsCode0
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model EvaluationCode0
Inferring the Reader: Guiding Automated Story Generation with Commonsense ReasoningCode0
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain AdaptationCode0
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document ParsingCode0
Balancing Label Quantity and Quality for Scalable ElicitationCode0
EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record GenerationCode0
CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse RelationsCode0
Effective Estimation of Deep Generative Language ModelsCode0
Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language ModelCode0
Information Guided Regularization for Fine-tuning Language ModelsCode0
Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and ContextCode0
CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in BlockchainCode0
Specializing Unsupervised Pretraining Models for Word-Level Semantic SimilarityCode0
CodeBenchGen: Creating Scalable Execution-based Code Generation BenchmarksCode0
Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language ModelsCode0
InPars-Light: Cost-Effective Unsupervised Training of Efficient RankersCode0
InRanker: Distilled Rankers for Zero-shot Information RetrievalCode0
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided SearchCode0
CodeEditor: Learning to Edit Source Code with Pre-trained ModelsCode0
INSPECT: Intrinsic and Systematic Probing Evaluation for Code TransformersCode0
Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative WritingCode0
A Masked Segmental Language Model for Unsupervised Natural Language SegmentationCode0
Instance Regularization for Discriminative Language Model Pre-trainingCode0
Effective Representation for Easy-First Dependency ParsingCode0
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity RecognitionCode0
Instruction-tuned Language Models are Better Knowledge LearnersCode0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language ModelsCode0
Integrated Semantic and Phonetic Post-correction for Chinese Speech RecognitionCode0
Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'Code0
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Bangla Grammatical Error Detection Using T5 Transformer ModelCode0
CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code ModelingCode0
Efficacy of Language Model Self-Play in Non-Zero-Sum GamesCode0
CodeKGC: Code Language Model for Generative Knowledge Graph ConstructionCode0
Integrating Large Language Models in Causal Discovery: A Statistical Causal ApproachCode0
Integrating Linguistic Theory and Neural Language ModelsCode0
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-DiversityCode0
Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge FeaturesCode0
Efficient and Long-Tailed Generalization for Pre-trained Vision-Language ModelCode0
Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language ModelingCode0
Barack's Wife Hillary: Using Knowledge Graphs for Fact-Aware Language ModelingCode0
HBTP: Heuristic Behavior Tree Planning with Large Language Model ReasoningCode0
Efficient Black-Box Adversarial Attacks on Neural Text DetectorsCode0
Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant ContentCode0
Efficient Contextualized Representation: Language Model Pruning for Sequence LabelingCode0
Code Soliloquies for Accurate Calculations in Large Language ModelsCode0
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language ModelCode0
A mean teacher algorithm for unlearning of language 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