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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9851–9900 of 17610 papers

TitleStatusHype
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning—0
Resolving Crash Bugs via Large Language Models: An Empirical Study—0
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit—0
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge BaseCode0
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated ContentCode0
Context-Driven Interactive Query Simulations Based on Generative Large Language ModelsCode0
InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs—0
Challenges with unsupervised LLM knowledge discovery—0
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent—0
ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)—0
Leveraging Language ID to Calculate Intermediate CTC Loss for Enhanced Code-Switching Speech Recognition—0
LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language—0
Student as an Inherent Denoiser of Noisy Teacher—0
Language Modeling on a SpiNNaker 2 Neuromorphic Chip—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
Successor Heads: Recurring, Interpretable Attention Heads In The Wild—0
Pixel Aligned Language Models—0
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers—0
Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language InferenceCode0
Identifying Planetary Names in Astronomy Papers: A Multi-Step Approach—0
CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation—0
Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation—0
Breaking the Silence: the Threats of Using LLMs in Software EngineeringCode0
Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media DataCode0
Contractive error feedback for gradient compression—0
Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning—0
Assessing GPT4-V on Structured Reasoning Tasks—0
A Foundational Multimodal Vision Language AI Assistant for Human Pathology—0
Large Language Model Enhanced Multi-Agent Systems for 6G Communications—0
Synocene, Beyond the Anthropocene: De-Anthropocentralising Human-Nature-AI Interaction—0
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling—0
Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-LearningCode0
SCCA: Shifted Cross Chunk Attention for long contextual semantic expansion—0
The GUA-Speech System Description for CNVSRC Challenge 2023—0
SM70: A Large Language Model for Medical Devices—0
LLM in a flash: Efficient Large Language Model Inference with Limited Memory—0
Translating Natural Language Queries to SQL Using the T5 Model—0
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment—0
Classifying complex documents: comparing bespoke solutions to large language models—0
A dynamical clipping approach with task feedback for Proximal Policy OptimizationCode0
ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity—0
Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling—0
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation—0
Rethinking the Instruction Quality: LIFT is What You Need—0
Vision-language Assisted Attribute Learning—0
Where exactly does contextualization in a PLM happen?—0
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning—0
PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language ModelsCode0
METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities—0
Decoupling SQL Query Hardness Parsing for Text-to-SQL—0
Show:102550
← PrevPage 198 of 353Next →
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