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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 53015350 of 17610 papers

TitleStatusHype
QWENDY: Gene Regulatory Network Inference Enhanced by Large Language Model and Transformer0
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration0
Prompt as Knowledge Bank: Boost Vision-language model via Structural Representation for zero-shot medical detection0
ZiGong 1.0: A Large Language Model for Financial Credit0
Dynamic Parallel Tree Search for Efficient LLM Reasoning0
A Framework for Evaluating Vision-Language Model Safety: Building Trust in AI for Public Sector Applications0
IPO: Your Language Model is Secretly a Preference ClassifierCode0
Echo: A Large Language Model with Temporal Episodic Memory0
Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents0
Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction0
Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction TuningCode0
ESPnet-SpeechLM: An Open Speech Language Model Toolkit0
DReSD: Dense Retrieval for Speculative Decoding0
Coherency Improved Explainable Recommendation via Large Language Model0
Bridging vision language model (VLM) evaluation gaps with a framework for scalable and cost-effective benchmark generation0
Forecasting Frontier Language Model Agent Capabilities0
Enhancing RWKV-based Language Models for Long-Sequence Text GenerationCode0
Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses0
Chitrarth: Bridging Vision and Language for a Billion People0
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model TrainingCode0
Machine-generated text detection prevents language model collapseCode0
A general language model for peptide identificationCode0
Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience InterviewsCode0
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models0
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System0
LEDD: Large Language Model-Empowered Data Discovery in Data Lakes0
Tight Clusters Make Specialized ExpertsCode0
Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable ExplanationsCode0
R^3Mem: Bridging Memory Retention and Retrieval via Reversible Compression0
MOVE: A Mixture-of-Vision-Encoders Approach for Domain-Focused Vision-Language Processing0
Optimizing Singular Spectrum for Large Language Model Compression0
Rapid Word Learning Through Meta In-Context Learning0
SR-LLM: Rethinking the Structured Representation in Large Language Model0
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness0
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling0
Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic SimilarityCode0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison0
Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generationCode0
HPS: Hard Preference Sampling for Human Preference Alignment0
AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain RecommendationsCode0
Flow-based generative models as iterative algorithms in probability space0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep HealthCode0
Complex Ontology Matching with Large Language Model Embeddings0
Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder0
Event Segmentation Applications in Large Language Model Enabled Automated Recall Assessments0
Autellix: An Efficient Serving Engine for LLM Agents as General Programs0
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models0
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences0
Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval0
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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