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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11011150 of 17610 papers

TitleStatusHype
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent EducationCode2
ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior ConstraintsCode2
LP-MusicCaps: LLM-Based Pseudo Music CaptioningCode2
Distilled Feature Fields Enable Few-Shot Language-Guided ManipulationCode2
TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormerCode2
A Systematic Survey of Prompt Engineering on Vision-Language Foundation ModelsCode2
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Planting a SEED of Vision in Large Language ModelCode2
Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language ModellingCode2
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge GraphCode2
Drive Like a Human: Rethinking Autonomous Driving with Large Language ModelsCode2
Generating Benchmarks for Factuality Evaluation of Language ModelsCode2
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language ModelsCode2
GPT4RoI: Instruction Tuning Large Language Model on Region-of-InterestCode2
Lost in the Middle: How Language Models Use Long ContextsCode2
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?Code2
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information RetrievalCode2
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained TransformerCode2
Provable Robust Watermarking for AI-Generated TextCode2
Most Language Models can be Poets too: An AI Writing Assistant and Constrained Text Generation StudioCode2
Towards Language Models That Can See: Computer Vision Through the LENS of Natural LanguageCode2
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide ResolutionCode2
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language ModelsCode2
RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote SensingCode2
XrayGPT: Chest Radiographs Summarization using Medical Vision-Language ModelsCode2
Valley: Video Assistant with Large Language model Enhanced abilitYCode2
K2: A Foundation Language Model for Geoscience Knowledge Understanding and UtilizationCode2
RETA-LLM: A Retrieval-Augmented Large Language Model ToolkitCode2
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for FinanceCode2
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning OptimizationCode2
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and BenchmarksCode2
ModuleFormer: Modularity Emerges from Mixture-of-ExpertsCode2
Inference-Time Intervention: Eliciting Truthful Answers from a Language ModelCode2
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight CompressionCode2
User Behavior Simulation with Large Language Model based AgentsCode2
Fine-Grained Human Feedback Gives Better Rewards for Language Model TrainingCode2
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised TrainingCode2
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
Blockwise Parallel Transformer for Large Context ModelsCode2
Multiscale Positive-Unlabeled Detection of AI-Generated TextsCode2
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and DatasetCode2
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model ApplicationCode2
Language Models Can Improve Event Prediction by Few-Shot Abductive ReasoningCode2
Adapting Language Models to Compress ContextsCode2
ExpertPrompting: Instructing Large Language Models to be Distinguished ExpertsCode2
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-trainingCode2
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language ModelsCode2
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text GenerationCode2
Improving Factuality and Reasoning in Language Models through Multiagent DebateCode2
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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