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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 31013150 of 17610 papers

TitleStatusHype
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property PredictionCode1
Domain-Agnostic Molecular Generation with Chemical FeedbackCode1
BioBERT: a pre-trained biomedical language representation model for biomedical text miningCode1
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding MatchingCode1
BioBART: Pretraining and Evaluation of A Biomedical Generative Language ModelCode1
Six Dragons Fly Again: Reviving 15th-Century Korean Court Music with Transformers and Novel EncodingCode1
Knowledge-Augmented Language Models for Cause-Effect Relation ClassificationCode1
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!Code1
COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust LearningCode1
A Large Scale Search Dataset for Unbiased Learning to RankCode1
Enhancing Clinical BERT Embedding using a Biomedical Knowledge BaseCode1
Code4Struct: Code Generation for Few-Shot Event Structure PredictionCode1
CodeArt: Better Code Models by Attention Regularization When Symbols Are LackingCode1
Salmon: A Suite for Acoustic Language Model EvaluationCode1
Espresso: A Fast End-to-end Neural Speech Recognition ToolkitCode1
Emotion-Aware Transformer Encoder for Empathetic Dialogue GenerationCode1
Empower Entity Set Expansion via Language Model ProbingCode1
EMO: Earth Mover Distance Optimization for Auto-Regressive Language ModelingCode1
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
EmojiLM: Modeling the New Emoji LanguageCode1
Empowering Large Language Model Agents through Action LearningCode1
MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual PropertyCode1
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming SequencesCode1
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language ModelsCode1
eMLM: A New Pre-training Objective for Emotion Related TasksCode1
Emergence of Social Norms in Generative Agent Societies: Principles and ArchitectureCode1
Emergent Analogical Reasoning in Large Language ModelsCode1
EMMA: Efficient Visual Alignment in Multi-Modal LLMsCode1
Mukayese: Turkish NLP Strikes BackCode1
CodeIt: Self-Improving Language Models with Prioritized Hindsight ReplayCode1
Multi-Agent Collaborative Data Selection for Efficient LLM PretrainingCode1
Multi-event Video-Text RetrievalCode1
Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language ModelsCode1
Empowering Large Language Model for Continual Video Question Answering with Collaborative PromptingCode1
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in TextCode1
Multilingual Mathematical AutoformalizationCode1
Multilingual Multi-Figurative Language DetectionCode1
Augmenting Interpretable Models with LLMs during TrainingCode1
Coder Reviewer Reranking for Code GenerationCode1
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text GenerationCode1
Elephants Never Forget: Testing Language Models for Memorization of Tabular DataCode1
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language ModelsCode1
ELI5: Long Form Question AnsweringCode1
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language ModelCode1
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language ModelCode1
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application WithCode1
Modular Multimodal Machine Learning for Extraction of Theorems and Proofs in Long Scientific Documents (Extended Version)Code1
Common Sense Enhanced Knowledge-based Recommendation with Large Language ModelCode1
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt VerbalizerCode1
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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