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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 551600 of 17610 papers

TitleStatusHype
Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel DecodingCode2
CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code GenerationCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Efficient Speech Language Modeling via Energy Distance in Continuous Latent SpaceCode2
SLOT: Sample-specific Language Model Optimization at Test-timeCode2
LifelongAgentBench: Evaluating LLM Agents as Lifelong LearnersCode2
Demystifying and Enhancing the Efficiency of Large Language Model Based Search AgentsCode2
WorldPM: Scaling Human Preference ModelingCode2
Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and EnhancementCode2
Behind Maya: Building a Multilingual Vision Language ModelCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
GuidedQuant: Large Language Model Quantization via Exploiting End Loss GuidanceCode2
MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based AgentsCode2
RWKV-X: A Linear Complexity Hybrid Language ModelCode2
Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information AnalysisCode2
The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single TransformerCode2
ClinicalGPT-R1: Pushing reasoning capability of generalist disease diagnosis with large language modelCode2
Vision-Language Model for Object Detection and Segmentation: A Review and EvaluationCode2
SegEarth-R1: Geospatial Pixel Reasoning via Large Language ModelCode2
PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language ModelsCode2
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video SegmentationCode2
TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language ModelingCode2
Scaling Video-Language Models to 10K Frames via Hierarchical Differential DistillationCode2
Unicorn: Text-Only Data Synthesis for Vision Language Model TrainingCode2
Mobile-VideoGPT: Fast and Accurate Video Understanding Language ModelCode2
Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face DetectorCode2
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
MC-LLaVA: Multi-Concept Personalized Vision-Language ModelCode2
CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application VulnerabilitiesCode2
FastCuRL: Curriculum Reinforcement Learning with Progressive Context Extension for Efficient Training R1-like Reasoning ModelsCode2
Modifying Large Language Model Post-Training for Diverse Creative WritingCode2
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language ModelCode2
VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-TuningCode2
Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM KernelsCode2
MaTVLM: Hybrid Mamba-Transformer for Efficient Vision-Language ModelingCode2
Generative Modeling for Mathematical DiscoveryCode2
GroundingSuite: Measuring Complex Multi-Granular Pixel GroundingCode2
OR-LLM-Agent: Automating Modeling and Solving of Operations Research Optimization Problem with Reasoning Large Language ModelCode2
Mellow: a small audio language model for reasoningCode2
LongProLIP: A Probabilistic Vision-Language Model with Long Context TextCode2
When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token PruningCode2
DiffCLIP: Differential Attention Meets CLIPCode2
Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language ModelCode2
A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information RetrievalCode2
PromptPex: Automatic Test Generation for Language Model PromptsCode2
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLMCode2
An Egocentric Vision-Language Model based Portable Real-time Smart AssistantCode2
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language ModelCode2
Scaling Rich Style-Prompted Text-to-Speech DatasetsCode2
Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking CapabilitiesCode2
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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