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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 44014450 of 17610 papers

TitleStatusHype
XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI ApproachCode0
How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language ModelsCode0
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction0
When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world EnvironmentsCode4
Qwen2-Audio Technical ReportCode7
MMM: Multilingual Mutual Reinforcement Effect Mix Datasets & Test with Open-domain Information Extraction Large Language Models0
Enhancing Medication Recommendation with LLM Text Representation0
DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading SystemsCode2
Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond0
Large Language Model-based FMRI Encoding of Language Functions for Subjects with Neurocognitive Disorder0
OVLW-DETR: Open-Vocabulary Light-Weighted Detection TransformerCode3
Qwen2 Technical ReportCode13
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework0
How and where does CLIP process negation?0
An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use CasesCode3
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together0
GRUtopia: Dream General Robots in a City at ScaleCode5
Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented GenerationCode2
Can Textual Semantics Mitigate Sounding Object Segmentation Preference?Code0
BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy0
Quantized Prompt for Efficient Generalization of Vision-Language ModelsCode0
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation0
On Large Language Model Continual UnlearningCode1
Multi-Granularity Semantic Revision for Large Language Model Distillation0
LeanQuant: Accurate Large Language Model Quantization with Loss-Error-Aware Grid0
AutoGRAMS: Autonomous Graphical Agent Modeling SoftwareCode2
Rapid Biomedical Research Classification: The Pandemic PACT Advanced Categorisation Engine0
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model0
ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step ReasoningCode1
Lean-STaR: Learning to Interleave Thinking and Proving0
Data Imputation using Large Language Model to Accelerate Recommendation System0
LAB-Bench: Measuring Capabilities of Language Models for Biology Research0
Bilingual Adaptation of Monolingual Foundation Models0
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language GuidanceCode1
Minimizing PLM-Based Few-Shot Intent DetectorsCode0
ICCV23 Visual-Dialog Emotion Explanation Challenge: SEU_309 Team Technical Report0
A Training Data Recipe to Accelerate A* Search with Language ModelsCode0
IoT-LM: Large Multisensory Language Models for the Internet of ThingsCode1
PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models0
Explanation is All You Need in Distillation: Mitigating Bias and Shortcut Learning0
GOFA: A Generative One-For-All Model for Joint Graph Language ModelingCode2
Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference0
Bridging Dictionary: AI-Generated Dictionary of Partisan Language Use0
How Chinese are Chinese Language Models? The Puzzling Lack of Language Policy in China's LLMs0
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documentsCode1
GPC: Generative and General Pathology Image Classifier0
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification0
Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary DetectionCode1
The Sociolinguistic Foundations of Language Modeling0
Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule 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