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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 67016750 of 17610 papers

TitleStatusHype
Deductive Additivity for Planning of Natural Language ProofsCode0
Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive MimickingCode0
Leveraging Large Language Models for Automated Dialogue AnalysisCode0
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation LearningCode0
SweCTRL-Mini: a data-transparent Transformer-based large language model for controllable text generation in SwedishCode0
Kyoto University Participation to WAT 2017Code0
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-trainingCode0
Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?Code0
Rethinking Code Refinement: Learning to Judge Code EfficiencyCode0
PsyEval: A Suite of Mental Health Related Tasks for Evaluating Large Language ModelsCode0
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-AwarenessCode0
Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language ModelCode0
Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event KnowledgeCode0
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemCode0
Scalable Second Order Optimization for Deep LearningCode0
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive BaselinesCode0
Syllable-aware Neural Language Models: A Failure to Beat Character-aware OnesCode0
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World EnvironmentsCode0
LaMemo: Language Modeling with Look-Ahead MemoryCode0
Searching for Best Practices in Medical Transcription with Large Language ModelCode0
Syllable Subword Tokens for Open Vocabulary Speech Recognition in MalayalamCode0
SyllabusQA: A Course Logistics Question Answering DatasetCode0
Symbolic Discovery of Optimization AlgorithmsCode0
PTransIPs: Identification of phosphorylation sites enhanced by protein PLM embeddingsCode0
Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRFCode0
Multilingual Normalization of Temporal Expressions with Masked Language ModelsCode0
Machine-in-the-Loop Rewriting for Creative Image CaptioningCode0
Grounded Language Agent for Product Search via Intelligent Web InteractionsCode0
Rethinking Channel Dimensions to Isolate Outliers for Low-bit Weight Quantization of Large Language ModelsCode0
On Effects of Steering Latent Representation for Large Language Model UnlearningCode0
Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable ExplanationsCode0
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment ClassificationCode0
Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language ModelsCode0
Kyoto-NMT: a Neural Machine Translation implementation in ChainerCode0
ScVLM: Enhancing Vision-Language Model for Safety-Critical Event UnderstandingCode0
Towards Community-Driven Agents for Machine Learning EngineeringCode0
RaTE: a Reproducible automatic Taxonomy Evaluation by Filling the GapCode0
PLDR-LLM: Large Language Model from Power Law Decoder RepresentationsCode0
Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language ModelCode0
One2set + Large Language Model: Best Partners for Keyphrase GenerationCode0
Neural Architecture Search with Reinforcement LearningCode0
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical RecordsCode0
On-Device Neural Language Model Based Word PredictionCode0
RATE: Causal Explainability of Reward Models with Imperfect CounterfactualsCode0
SynSUM -- Synthetic Benchmark with Structured and Unstructured Medical RecordsCode0
Multi-Lingual Question Generation with Language Agnostic Language ModelCode0
Punctuation Restoration for Singaporean Spoken Languages: English, Malay, and MandarinCode0
Towards Democratized Flood Risk Management: An Advanced AI Assistant Enabled by GPT-4 for Enhanced Interpretability and Public EngagementCode0
Punctuation Restoration Improves Structure Understanding Without SupervisionCode0
Show:102550
← PrevPage 135 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