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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4201–4250 of 17610 papers

TitleStatusHype
Event-Priori-Based Vision-Language Model for Efficient Visual Understanding—0
Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language ModelsCode0
Scaling Laws of Motion Forecasting and Planning -- A Technical Report—0
A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks—0
A Hybrid GA LLM Framework for Structured Task OptimizationCode0
AnnoDPO: Protein Functional Annotation Learning with Direct Preference OptimizationCode0
Automatic Speech Recognition of African American English: Lexical and Contextual Effects—0
An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design—0
Benchmarking Misuse Mitigation Against Covert AdversariesCode0
Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management—0
Voice Impression Control in Zero-Shot TTS—0
WhisQ: Cross-Modal Representation Learning for Text-to-Music MOS Prediction—0
Training-Free Query Optimization via LLM-Based Plan Similarity—0
Label-Context-Dependent Internal Language Model Estimation for CTC—0
Masked Language Models are Good Heterogeneous Graph GeneralizersCode0
PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time—0
The NTNU System at the S&I Challenge 2025 SLA Open Track—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Robust Few-Shot Vision-Language Model Adaptation—0
Sparse Autoencoders, Again?—0
MesaNet: Sequence Modeling by Locally Optimal Test-Time Training—0
Handle-based Mesh Deformation Guided By Vision Language Model—0
Clustering and Median Aggregation Improve Differentially Private Inference—0
Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic System—0
HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model TrainingCode0
Improving Low-Resource Morphological Inflection via Self-Supervised Objectives—0
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT—0
Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query ExpansionCode0
Accelerated Test-Time Scaling with Model-Free Speculative Sampling—0
HoliSafe: Holistic Safety Benchmarking and Modeling with Safety Meta Token for Vision-Language Model—0
E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
Unleashing Hour-Scale Video Training for Long Video-Language Understanding—0
Zeroth-Order Optimization Finds Flat Minima—0
Rectified Sparse Attention—0
Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems—0
LaF-GRPO: In-Situ Navigation Instruction Generation for the Visually Impaired via GRPO with LLM-as-Follower RewardCode0
MELABenchv1: Benchmarking Large Language Models against Smaller Fine-Tuned Models for Low-Resource Maltese NLP—0
Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering HateCode0
Phi-Omni-ST: A multimodal language model for direct speech-to-speech translation—0
MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at Scale—0
A Statistical Physics of Language Model Reasoning—0
EuroLLM-9B: Technical Report—0
Go-Browse: Training Web Agents with Structured Exploration—0
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model—0
Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement—0
"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation—0
Evaluating Large Language Model Capabilities in Assessing Spatial Econometrics Research—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