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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 11051–11100 of 17610 papers

TitleStatusHype
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model InfillingCode1
Hard Gate Knowledge Distillation -- Leverage Calibration for Robust and Reliable Language Model—0
Generative Prompt Tuning for Relation ClassificationCode1
Dissecting Deep Metric Learning Losses for Image-Text RetrievalCode0
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal ProofsCode1
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation—0
Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic AmbiguitiesCode0
Z-LaVI: Zero-Shot Language Solver Fueled by Visual ImaginationCode0
InforMask: Unsupervised Informative Masking for Language Model PretrainingCode1
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?Code0
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer—0
Graphemic Normalization of the Perso-Arabic ScriptCode0
A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT—0
Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long SequencesCode1
Is Encoder-Decoder Redundant for Neural Machine Translation?—0
LittleBird: Efficient Faster & Longer Transformer for Question Answering—0
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling—0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Transcending Scaling Laws with 0.1% Extra Compute—0
Tele-Knowledge Pre-training for Fault AnalysisCode1
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions—0
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and MiningCode4
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-LearningCode0
Improving Aspect Sentiment Quad Prediction via Template-Order Data AugmentationCode1
Language Model Decomposition: Quantifying the Dependency and Correlation of Language ModelsCode1
Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages—0
The Devil in Linear TransformerCode1
TabLLM: Few-shot Classification of Tabular Data with Large Language ModelsCode2
Language Detoxification with Attribute-Discriminative Latent SpaceCode0
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters—0
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models—0
Aligning MAGMA by Few-Shot Learning and Finetuning—0
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared TaskCode0
Fine-mixing: Mitigating Backdoors in Fine-tuned Language ModelsCode8
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared TaskCode0
Systematicity in GPT-3's Interpretation of Novel English Noun Compounds—0
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment AnalysisCode1
The Tail Wagging the Dog: Dataset Construction Biases of Social Bias BenchmarksCode0
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation—0
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning—0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
Deep Bidirectional Language-Knowledge Graph PretrainingCode2
Continuous Pseudo-Labeling from the Start—0
Prompting GPT-3 To Be ReliableCode1
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation—0
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task GeneralizationCode0
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve ThemCode2
RARR: Researching and Revising What Language Models Say, Using Language ModelsCode1
Acoustic-aware Non-autoregressive Spell Correction with Mask Sample Decoding—0
Show:102550
← PrevPage 222 of 353Next →
All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified