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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 19011950 of 17610 papers

TitleStatusHype
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
A Surprisingly Robust Trick for Winograd Schema ChallengeCode1
DziriBERT: a Pre-trained Language Model for the Algerian DialectCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
How far is Language Model from 100% Few-shot Named Entity Recognition in Medical DomainCode1
How to Fine-Tune BERT for Text Classification?Code1
Housekeep: Tidying Virtual Households using Commonsense ReasoningCode1
HOP: History-and-Order Aware Pre-training for Vision-and-Language NavigationCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
Salmon: A Suite for Acoustic Language Model EvaluationCode1
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
Analysing The Impact of Sequence Composition on Language Model Pre-TrainingCode1
Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image UnderstandingCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials ScienceCode1
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language modelCode1
A Large Scale Search Dataset for Unbiased Learning to RankCode1
Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code GenerationCode1
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language ModelCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
How does the pre-training objective affect what large language models learn about linguistic properties?Code1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
AutoScrum: Automating Project Planning Using Large Language ModelsCode1
Effective Attention Sheds Light On InterpretabilityCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
hmBERT: Historical Multilingual Language Models for Named Entity RecognitionCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
CriticEval: Evaluating Large Language Model as CriticCode1
HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language ModelsCode1
Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event ExtractionCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
CDLM: Cross-Document Language ModelingCode1
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
Efficient Content-Based Sparse Attention with Routing TransformersCode1
History Matters: Temporal Knowledge Editing in Large Language ModelCode1
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognitionCode1
Language Models Encode the Value of Numbers LinearlyCode1
Language Models with Image Descriptors are Strong Few-Shot Video-Language LearnersCode1
Static and Animated 3D Scene Generation from Free-form Text DescriptionsCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-GenerationCode1
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test ConstructionCode1
A Study of Generative Large Language Model for Medical Research and HealthcareCode1
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and DistillationCode1
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and BiasCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Show:102550
← PrevPage 39 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