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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 701750 of 17610 papers

TitleStatusHype
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMsCode2
Q-VLM: Post-training Quantization for Large Vision-Language ModelsCode2
OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring ModelingCode2
Sylber: Syllabic Embedding Representation of Speech from Raw AudioCode2
Towards Interpreting Visual Information Processing in Vision-Language ModelsCode2
Compositional Entailment Learning for Hyperbolic Vision-Language ModelsCode2
Think While You Generate: Discrete Diffusion with Planned DenoisingCode2
BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile ManipulationCode2
PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse SamplingCode2
TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer TokensCode2
Differential TransformerCode2
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention CausalityCode2
GenSim: A General Social Simulation Platform with Large Language Model based AgentsCode2
SyllableLM: Learning Coarse Semantic Units for Speech Language ModelsCode2
A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language ModelsCode2
Autoregressive Action Sequence Learning for Robotic ManipulationCode2
NNetscape Navigator: Complex Demonstrations for Web Agents Without a DemonstratorCode2
Leopard: A Vision Language Model For Text-Rich Multi-Image TasksCode2
Robin3D: Improving 3D Large Language Model via Robust Instruction TuningCode2
FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"Code2
DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning DataCode2
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential RecommendationCode2
One Token to Seg Them All: Language Instructed Reasoning Segmentation in VideosCode2
Control Industrial Automation System with Large Language Model AgentsCode2
Empirical Asset Pricing with Large Language Model AgentsCode2
Small Language Models: Survey, Measurements, and InsightsCode2
EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG ModelCode2
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI UnderstandingCode2
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and ManagementCode2
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model InitializationCode2
Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model ReasoningCode2
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making FrameworkCode2
AutoVerus: Automated Proof Generation for Rust CodeCode2
LLaQo: Towards a Query-Based Coach in Expressive Music Performance AssessmentCode2
Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile InstructionsCode2
Synthetic continued pretrainingCode2
MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous DrivingCode2
DetailCLIP: Detail-Oriented CLIP for Fine-Grained TasksCode2
TransformerRanker: A Tool for Efficiently Finding the Best-Suited Language Models for Downstream Classification TasksCode2
The AdEMAMix Optimizer: Better, Faster, OlderCode2
Language Model Powered Digital Biology with BRADCode2
EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio Captioning PerformanceCode2
Sample-Efficient Diffusion for Text-To-Speech SynthesisCode2
SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression SegmentationCode2
MemLong: Memory-Augmented Retrieval for Long Text ModelingCode2
Law of Vision Representation in MLLMsCode2
Efficient LLM Scheduling by Learning to RankCode2
LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks YetCode2
MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models AgentsCode2
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token EmbeddingsCode2
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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