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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 901950 of 17610 papers

TitleStatusHype
Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language ModelCode2
OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution FittingCode2
Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion TokensCode2
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model AgentsCode2
Paint by Inpaint: Learning to Add Image Objects by Removing Them FirstCode2
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision GeneralistsCode2
PaLM: Scaling Language Modeling with PathwaysCode2
PALO: A Polyglot Large Multimodal Model for 5B PeopleCode2
In-Context Retrieval-Augmented Language ModelsCode2
PAPILLON: Privacy Preservation from Internet-based and Local Language Model EnsemblesCode2
In-Context Language Learning: Architectures and AlgorithmsCode2
Beyond Next Token Prediction: Patch-Level Training for Large Language ModelsCode2
in2IN: Leveraging individual Information to Generate Human INteractionsCode2
Inference-Time Intervention: Eliciting Truthful Answers from a Language ModelCode2
Implicit Neural Representation for Cooperative Low-light Image EnhancementCode2
BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile ManipulationCode2
Improved Representation Steering for Language ModelsCode2
DetailCLIP: Detail-Oriented CLIP for Fine-Grained TasksCode2
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementCode2
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
Improve Vision Language Model Chain-of-thought ReasoningCode2
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for FinanceCode2
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide ResolutionCode2
Hyena Hierarchy: Towards Larger Convolutional Language ModelsCode2
Model Editing as a Robust and Denoised variant of DPO: A Case Study on ToxicityCode2
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge GraphsCode2
Hungry Hungry Hippos: Towards Language Modeling with State Space ModelsCode2
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction FollowingCode2
Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language ModelCode2
IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script GenerationCode2
Improving Factuality and Reasoning in Language Models through Multiagent DebateCode2
Huatuo-26M, a Large-scale Chinese Medical QA DatasetCode2
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMsCode2
Calibrated Self-Rewarding Vision Language ModelsCode2
Collaborative Expert LLMs Guided Multi-Objective Molecular OptimizationCode2
How to Index Item IDs for Recommendation Foundation ModelsCode2
Holodeck: Language Guided Generation of 3D Embodied AI EnvironmentsCode2
Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMCode2
AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web PlatformsCode2
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained EvaluationCode2
Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially FastCode2
PromptDet: Towards Open-vocabulary Detection using Uncurated ImagesCode2
An empirical study of LLaMA3 quantization: from LLMs to MLLMsCode2
Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory PredictionCode2
Improving Language Model Negotiation with Self-Play and In-Context Learning from AI FeedbackCode2
DiffusionBERT: Improving Generative Masked Language Models with Diffusion ModelsCode2
Differential TransformerCode2
ProteinBERT: a universal deep-learning model of protein sequence and functionCode2
Provable Robust Watermarking for AI-Generated TextCode2
Introducing Visual Perception Token into Multimodal Large Language ModelCode2
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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