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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1510115150 of 17610 papers

TitleStatusHype
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein EngineeringCode0
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Stealth edits to large language modelsCode0
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information ExtractionCode0
The Crucial Role of Samplers in Online Direct Preference OptimizationCode0
PrOnto: Language Model Evaluations for 859 LanguagesCode0
Prompt Tuning or Fine-Tuning - Investigating Relational Knowledge in Pre-Trained Language ModelsCode0
LLM Safety Alignment is Divergence Estimation in DisguiseCode0
MST5 -- Multilingual Question Answering over Knowledge GraphsCode0
MSDT: Masked Language Model Scoring Defense in Text DomainCode0
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus InfectionCode0
Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language ModelsCode0
KALE: An Artwork Image Captioning System Augmented with Heterogeneous GraphCode0
MovSAM: A Single-image Moving Object Segmentation Framework Based on Deep ThinkingCode0
ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common SenseCode0
K-12BERT: BERT for K-12 educationCode0
MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion PriorCode0
Morphology Matters: A Multilingual Language Modeling AnalysisCode0
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuningCode0
Stepwise Alignment for Constrained Language Model Policy OptimizationCode0
Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model TutorsCode0
PromptShots at the FinNLP-2022 ERAI Tasks: Pairwise Comparison and Unsupervised RankingCode0
Rotational Unit of MemoryCode0
StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and StereotypesCode0
Round Trip Translation Defence against Large Language Model Jailbreaking AttacksCode0
Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model AdaptationCode0
Understanding the effects of language-specific class imbalance in multilingual fine-tuningCode0
Routing Networks and the Challenges of Modular and Compositional ComputationCode0
Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced RelevanceCode0
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of ExemplarsCode0
AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language ModelsCode0
Strings from the Library of Babel: Random Sampling as a Strong Baseline for Prompt OptimisationCode0
PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language ModelsCode0
Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue GenerationCode0
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
r-softmax: Generalized Softmax with Controllable Sparsity RateCode0
Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy ConstructionCode0
Prompt-enhanced Network for Hateful Meme ClassificationCode0
Prompt Engineering for Transformer-based Chemical Similarity Search Identifies Structurally Distinct Functional AnaloguesCode0
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model InferenceCode0
Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level TrainingCode0
R-Transformer: Recurrent Neural Network Enhanced TransformerCode0
RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience VisualizationCode0
MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized CollaborationCode0
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine ConflictCode0
PromptCL: Improving Event Representation via Prompt Template and Contrastive LearningCode0
Jasper: An End-to-End Convolutional Neural Acoustic ModelCode0
RUIE: Retrieval-based Unified Information Extraction using Large Language ModelCode0
Morfessor FlatCat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of MorphologyCode0
LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language ModelsCode0
Show:102550
← PrevPage 303 of 353Next →

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