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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 61516200 of 17610 papers

TitleStatusHype
PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary AlgorithmsCode0
Order-Independence Without Fine TuningCode0
Think Again Networks and the Delta LossCode0
Large Language Model for Science: A Study on P vs. NPCode0
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&ACode0
mu-Forcing: Training Variational Recurrent Autoencoders for Text GenerationCode0
Two/Too Simple Adaptations of Word2Vec for Syntax ProblemsCode0
Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text RecognitionCode0
Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMsCode0
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health RecordsCode0
Natural Language Decompositions of Implicit Content Enable Better Text RepresentationsCode0
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance DetectionCode0
SERENGETI: Massively Multilingual Language Models for AfricaCode0
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language ModelCode0
Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model PromptingCode0
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence ModellingCode0
Light Coreference Resolution for Russian with Hierarchical Discourse FeaturesCode0
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials ScienceCode0
Learning Semantic Textual Similarity via Topic-informed Discrete Latent VariablesCode0
PaLM: A Hybrid Parser and Language ModelCode0
SMARTFinRAG: Interactive Modularized Financial RAG BenchmarkCode0
Sequential Large Language Model-Based Hyper-parameter OptimizationCode0
Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language TasksCode0
Mukh-Oboyob: Stable Diffusion and BanglaBERT enhanced Bangla Text-to-Face SynthesisCode0
SMART: Submodular Data Mixture Strategy for Instruction TuningCode0
Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering HateCode0
SMASH at Qur’an QA 2022: Creating Better Faithful Data Splits for Low-resourced Question Answering ScenariosCode0
Revisiting Counterfactual Problems in Referring Expression ComprehensionCode0
NLQxform: A Language Model-based Question to SPARQL TransformerCode0
Large Product Key Memory for Pretrained Language ModelsCode0
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug DiscoveryCode0
Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media TextCode0
Sequence to Sequence -- Video to TextCode0
Sequence to sequence pretraining for a less-resourced Slovenian languageCode0
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification TasksCode0
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Third-Party Aligner for Neural Word AlignmentsCode0
Smoothing Entailment Graphs with Language ModelsCode0
Third-Party Language Model Performance Prediction from InstructionCode0
Sequence-to-Sequence Learning as Beam-Search OptimizationCode0
This Land is Your, My Land: Evaluating Geopolitical Biases in Language ModelsCode0
Reliable Academic Conference Question Answering: A Study Based on Large Language ModelCode0
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual DataCode0
Meta-Learning the Difference: Preparing Large Language Models for Efficient AdaptationCode0
Language Modeling Using Tensor TrainsCode0
SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence CompressionCode0
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language ModelCode0
TypedThinker: Typed Thinking Improves Large Language Model ReasoningCode0
PanGu-Coder: Program Synthesis with Function-Level Language ModelingCode0
RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health InterviewsCode0
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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