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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 3801–3850 of 17610 papers

TitleStatusHype
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?Code0
Towards Real-World Adverse Weather Image Restoration: Enhancing Clearness and Semantics with Vision-Language ModelsCode1
LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning—0
OLMoE: Open Mixture-of-Experts Language ModelsCode4
Agentic Society: Merging skeleton from real world and texture from Large Language ModelCode0
EEG-Language Modeling for Pathology Detection—0
The Compressor-Retriever Architecture for Language Model OSCode1
Balancing Performance and Efficiency: A Multimodal Large Language Model Pruning Method based Image Text Interaction—0
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning—0
User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
Co-Learning: Code Learning for Multi-Agent Reinforcement Collaborative Framework with Conversational Natural Language InterfacesCode1
Grounding Language Models in Autonomous Loco-manipulation Tasks—0
EnCLAP++: Analyzing the EnCLAP Framework for Optimizing Automated Audio Captioning PerformanceCode2
Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text InformationCode1
SCOPE: Sign Language Contextual Processing with Embedding from LLMsCode0
A Perspective on Literary Metaphor in the Context of Generative AI—0
DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image Editing—0
Imitating Language via Scalable Inverse Reinforcement Learning—0
Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&ACode0
SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression SegmentationCode2
Sample-Efficient Diffusion for Text-To-Speech SynthesisCode2
ContextCite: Attributing Model Generation to ContextCode3
Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
TinyAgent: Function Calling at the EdgeCode3
The Dark Side of Human Feedback: Poisoning Large Language Models via User Inputs—0
From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education—0
Testing and Evaluation of Large Language Models: Correctness, Non-Toxicity, and Fairness—0
FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language ModelCode1
OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography—0
MultiMath: Bridging Visual and Mathematical Reasoning for Large Language ModelsCode1
OnlySportsLM: Optimizing Sports-Domain Language Models with SOTA Performance under Billion ParametersCode0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based ApproachCode0
AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene UnderstandingCode0
Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach—0
Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage—0
Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language ModelCode3
InkubaLM: A small language model for low-resource African languages—0
Assessing Generative Language Models in Classification Tasks: Performance and Self-Evaluation Capabilities in the Environmental and Climate Change DomainCode0
Novel-WD: Exploring acquisition of Novel World Knowledge in LLMs Using Prefix-Tuning—0
Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging—0
MemLong: Memory-Augmented Retrieval for Long Text ModelingCode2
Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering—0
Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer—0
ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObility—0
WET: Overcoming Paraphrasing Vulnerabilities in Embeddings-as-a-Service with Linear Transformation WatermarksCode0
Logic Contrastive Reasoning with Lightweight Large Language Model for Math Word Problems—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified