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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 29513000 of 17610 papers

TitleStatusHype
What do Vision Transformers Learn? A Visual ExplorationCode1
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of ContextsCode1
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android BytecodeCode1
Learning Domain Invariant Prompt for Vision-Language ModelsCode1
SpeechLMScore: Evaluating speech generation using speech language modelCode1
A Generative Approach for Script Event Prediction via Contrastive Fine-tuningCode1
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language ModelsCode1
Nonparametric Masked Language ModelingCode1
UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge GraphCode1
Protein Language Models and Structure Prediction: Connection and ProgressionCode1
Coder Reviewer Reranking for Code GenerationCode1
Composition based oxidation state prediction of materials using deep learningCode1
Multi-Modal Few-Shot Temporal Action DetectionCode1
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion LearningCode1
Self-supervised vision-language pretraining for Medical visual question answeringCode1
Open-vocabulary Attribute DetectionCode1
Unified Multimodal Model with Unlikelihood Training for Visual DialogCode1
Visually Grounded Commonsense Knowledge AcquisitionCode1
MarianCG: a code generation transformer model inspired by machine translationCode1
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image ClassificationCode1
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in TextCode1
Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent AttentionCode1
Validating Large Language Models with ReLMCode1
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text SpottingCode1
Knowledge Graph Generation From TextCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
PromptCap: Prompt-Guided Task-Aware Image CaptioningCode1
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion PromptsCode1
ADEPT: A DEbiasing PrompT FrameworkCode1
Investigating Fairness Disparities in Peer Review: A Language Model Enhanced ApproachCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingCode1
KGLM: Integrating Knowledge Graph Structure in Language Models for Link PredictionCode1
Open-Vocabulary Argument Role Prediction for Event ExtractionCode1
Contextual information integration for stance detection via cross-attentionCode1
LMentry: A Language Model Benchmark of Elementary Language TasksCode1
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks AdaptivelyCode1
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language ModelCode1
Fine-Tuning Language Models via Epistemic Neural NetworksCode1
T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5Code1
SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular ControlCode1
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic ChangeCode1
L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep LearningCode1
Differentiable Data Augmentation for Contrastive Sentence Representation LearningCode1
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless TrainingCode1
RoChBert: Towards Robust BERT Fine-tuning for ChineseCode1
Leveraging Label Correlations in a Multi-label Setting: A Case Study in EmotionCode1
COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust LearningCode1
Truncation Sampling as Language Model DesmoothingCode1
Open-vocabulary Semantic Segmentation with Frozen Vision-Language ModelsCode1
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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