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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 59015950 of 17610 papers

TitleStatusHype
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense RetrievalCode0
Morphology Matters: A Multilingual Language Modeling AnalysisCode0
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language ModelsCode0
ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common SenseCode0
Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity RecognitionCode0
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive SummarizationCode0
Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image RetrievalCode0
Tradeoffs Between Alignment and Helpfulness in Language Models with Representation EngineeringCode0
Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender SystemsCode0
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language ModelCode0
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design DecisionsCode0
LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot SystemsCode0
Trade-Offs Between Fairness and Privacy in Language ModelingCode0
Language Model-Based Paired Variational Autoencoders for Robotic Language LearningCode0
Not all parameters are born equal: Attention is mostly what you needCode0
Uncovering Intermediate Variables in Transformers using Circuit ProbingCode0
Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language ModelsCode0
The Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsCode0
Robustness of Learning from Task InstructionsCode0
Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language ReasoningCode0
Primer: Searching for Efficient Transformers for Language ModelingCode0
Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice QuestionsCode0
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained RelationshipsCode0
The Hidden Space of Transformer Language AdaptersCode0
NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-FlyCode0
Large Language Model Recall Uncertainty is Modulated by the Fan EffectCode0
NESTLE: a No-Code Tool for Statistical Analysis of Legal CorpusCode0
Priors for symbolic regressionCode0
Robust Conversational Agents against Imperceptible Toxicity TriggersCode0
Train-Attention: Meta-Learning Where to Focus in Continual Knowledge LearningCode0
The Impact of Element Ordering on LM Agent PerformanceCode0
Towards Logically Sound Natural Language Reasoning with Logic-Enhanced Language Model AgentsCode0
Computational Reasoning of Large Language ModelsCode0
LT-LM: a novel non-autoregressive language model for single-shot lattice rescoringCode0
debiaSAE: Benchmarking and Mitigating Vision-Language Model BiasCode0
The impact of responding to patient messages with large language model assistanceCode0
The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGACode0
The Importance of Being Recurrent for Modeling Hierarchical StructureCode0
MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion PriorCode0
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause AttachmentCode0
Mapping and Cleaning Open Commonsense Knowledge Bases with Generative TranslationCode0
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short OnesCode0
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph StructuresCode0
Recurrent Batch NormalizationCode0
The Influence of Context on Sentence Acceptability JudgementsCode0
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model TrainingCode0
Learning Recurrent Binary/Ternary WeightsCode0
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in TransformersCode0
MAPLE: Mobile App Prediction Leveraging Large Language Model EmbeddingsCode0
Training and Generating Neural Networks in Compressed Weight SpaceCode0
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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