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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 23512400 of 17610 papers

TitleStatusHype
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
CDLM: Cross-Document Language ModelingCode1
InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction OperatorsCode1
Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility PredictionCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Injecting Numerical Reasoning Skills into Language ModelsCode1
CriticEval: Evaluating Large Language Model as CriticCode1
-former: Infinite Memory TransformerCode1
AgentMove: Predicting Human Mobility Anywhere Using Large Language Model based Agentic FrameworkCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
-former: Infinite Memory TransformerCode1
INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval ModelsCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
InfoLM: A New Metric to Evaluate Summarization & Data2Text GenerationCode1
InfoCSE: Information-aggregated Contrastive Learning of Sentence EmbeddingsCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language ModelsCode1
InforMask: Unsupervised Informative Masking for Language Model PretrainingCode1
CrAM: A Compression-Aware MinimizerCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
Agentic Skill DiscoveryCode1
INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language ModelCode1
RealFormer: Transformer Likes Residual AttentionCode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
CPM: A Large-scale Generative Chinese Pre-trained Language ModelCode1
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and GenerationCode1
CoVR-2: Automatic Data Construction for Composed Video RetrievalCode1
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuningCode1
CPLLM: Clinical Prediction with Large Language ModelsCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
Inference with Reference: Lossless Acceleration of Large Language ModelsCode1
InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language ModelCode1
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-TrainingCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary InitializationCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
InferCept: Efficient Intercept Support for Augmented Large Language Model InferenceCode1
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuningCode1
Incorporating Large Language Models into Production Systems for Enhanced Task Automation and FlexibilityCode1
ApiQ: Finetuning of 2-Bit Quantized Large Language ModelCode1
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout GroupsCode1
cosFormer: Rethinking Softmax in AttentionCode1
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human AnnotationCode1
Inductive Entity Representations from Text via Link PredictionCode1
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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