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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 37513800 of 17610 papers

TitleStatusHype
MambaLRP: Explaining Selective State Space Sequence ModelsCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Towards Open-Ended Visual Recognition with Large Language ModelCode1
Towards Open-World Text-Guided Face Image Generation and ManipulationCode1
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement PredictionCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Mark My Words: Analyzing and Evaluating Language Model WatermarksCode1
Declaration-based Prompt Tuning for Visual Question AnsweringCode1
Making Language Models Better Tool Learners with Execution FeedbackCode1
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language ModelCode1
MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based FinetuningCode1
Decision Transformer: Reinforcement Learning via Sequence ModelingCode1
CriticEval: Evaluating Large Language Model as CriticCode1
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text PretrainingCode1
Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI ModelsCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Making Retrieval-Augmented Language Models Robust to Irrelevant ContextCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned DecisionCode1
Traditional and context-specific spam detection in low resource settingsCode1
DebUnc: Improving Large Language Model Agent Communication With Uncertainty MetricsCode1
TrafficSafetyGPT: Tuning a Pre-trained Large Language Model to a Domain-Specific Expert in Transportation SafetyCode1
Decouple knowledge from parameters for plug-and-play language modelingCode1
Debiasing Methods in Natural Language Understanding Make Bias More AccessibleCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
Length Generalization of Causal Transformers without Position EncodingCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Deciphering antibody affinity maturation with language models and weakly supervised learningCode1
CDLM: Cross-Document Language ModelingCode1
AutomaTikZ: Text-Guided Synthesis of Scientific Vector Graphics with TikZCode1
DenseCLIP: Language-Guided Dense Prediction with Context-Aware PromptingCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak DecoderCode1
Markovian Transformers for Informative Language ModelingCode1
AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-TuningCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!Code1
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small ModelsCode1
Data-to-Text Generation with Iterative Text EditingCode1
Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt DependenceCode1
Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fatCode1
Training on the Test Task Confounds Evaluation and EmergenceCode1
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP StandardsCode1
Train No Evil: Selective Masking for Task-Guided Pre-TrainingCode1
Leveraging Label Correlations in a Multi-label Setting: A Case Study in EmotionCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
BEND: Benchmarking DNA Language Models on biologically meaningful tasksCode1
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller 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