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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 39013950 of 17610 papers

TitleStatusHype
Advancing High Resolution Vision-Language Models in BiomedicineCode1
Effective Attention Sheds Light On InterpretabilityCode1
EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROADCode1
ECRECer: Enzyme Commission Number Recommendation and Benchmarking based on Multiagent Dual-core LearningCode1
Unifying Vision-and-Language Tasks via Text GenerationCode1
UniGLM: Training One Unified Language Model for Text-Attributed Graph EmbeddingCode1
Effectiveness of self-supervised pre-training for speech recognitionCode1
ECAMP: Entity-centered Context-aware Medical Vision Language Pre-trainingCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
UniProcessor: A Text-induced Unified Low-level Image ProcessorCode1
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric ApproachCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and GenerationCode1
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain GeneralizationCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray imagesCode1
Unlocking State-Tracking in Linear RNNs Through Negative EigenvaluesCode1
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage RetrievalCode1
CycleFormer : TSP Solver Based on Language ModelingCode1
Unsupervised Dependency Graph NetworkCode1
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine TranslationCode1
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBenchCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
Unsupervised pre-training of graph transformers on patient population graphsCode1
EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence InformationCode1
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic ParsingCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition ExtractionCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across ModalitiesCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
DALE: Generative Data Augmentation for Low-Resource Legal NLPCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Dynamic Grained Encoder for Vision TransformersCode1
Accurate identification of bacteriophages from metagenomic data using TransformerCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGsCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
Beheshti-NER: Persian Named Entity Recognition Using BERTCode1
VaLiD: Mitigating the Hallucination of Large Vision Language Models by Visual Layer Fusion Contrastive DecodingCode1
Value Augmented Sampling for Language Model Alignment and PersonalizationCode1
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documentsCode1
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image SegmentationCode1
Advancing Beyond Identification: Multi-bit Watermark for Large Language ModelsCode1
DARTS: Differentiable Architecture SearchCode1
Bayesian Prompt Learning for Image-Language Model GeneralizationCode1
Dynamic Contextualized Word EmbeddingsCode1
Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural NetworksCode1
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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