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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12511300 of 17610 papers

TitleStatusHype
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning AbilitiesCode2
Generative Modeling for Mathematical DiscoveryCode2
MaTVLM: Hybrid Mamba-Transformer for Efficient Vision-Language ModelingCode2
From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning TasksCode2
A Touch, Vision, and Language Dataset for Multimodal AlignmentCode2
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
CogView2: Faster and Better Text-to-Image Generation via Hierarchical TransformersCode2
From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context ExamplesCode2
A Training-free LLM-based Approach to General Chinese Character Error CorrectionCode2
Mega: Moving Average Equipped Gated AttentionCode2
Collaborative Expert LLMs Guided Multi-Objective Molecular OptimizationCode2
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model ParallelismCode2
Full-Duplex-Bench: A Benchmark to Evaluate Full-duplex Spoken Dialogue Models on Turn-taking CapabilitiesCode2
Forgetting Transformer: Softmax Attention with a Forget GateCode2
A Systematic Study of Cross-Layer KV Sharing for Efficient LLM InferenceCode2
Memory MosaicsCode2
Agent-R: Training Language Model Agents to Reflect via Iterative Self-TrainingCode2
Metadata Conditioning Accelerates Language Model Pre-trainingCode2
MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-ProcessingCode2
Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language ModelCode2
Formal Mathematics Statement Curriculum LearningCode2
A Systematic Survey of Prompt Engineering on Vision-Language Foundation ModelsCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Generative Pre-trained Speech Language Model with Efficient Hierarchical TransformerCode2
Composed Image Retrieval for Remote SensingCode2
Asynchronous Large Language Model Enhanced Planner for Autonomous DrivingCode2
AgentSims: An Open-Source Sandbox for Large Language Model EvaluationCode2
FLAME: Financial Large-Language Model Assessment and Metrics EvaluationCode2
An Egocentric Vision-Language Model based Portable Real-time Smart AssistantCode2
FLAIR: VLM with Fine-grained Language-informed Image RepresentationsCode2
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsCode2
A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language ModelCode2
ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior ConstraintsCode2
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language ModelsCode2
Concept Bottleneck Language Models For protein designCode2
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical EnvironmentsCode2
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI UnderstandingCode2
Modifying Large Language Model Post-Training for Diverse Creative WritingCode2
MoEUT: Mixture-of-Experts Universal TransformersCode2
A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information RetrievalCode2
A Survey of Multimodal Large Language Model from A Data-centric PerspectiveCode2
FIRST: Faster Improved Listwise Reranking with Single Token DecodingCode2
Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language ModelsCode2
Montessori-Instruct: Generate Influential Training Data Tailored for Student LearningCode2
DiffCSE: Difference-based Contrastive Learning for Sentence EmbeddingsCode2
DiffArtist: Towards Structure and Appearance Controllable Image StylizationCode2
A Survey of Graph Meets Large Language Model: Progress and Future DirectionsCode2
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character DesignCode2
Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language ModelsCode2
Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at ScaleCode2
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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