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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2701–2750 of 17610 papers

TitleStatusHype
Prompt-enhanced Network for Hateful Meme ClassificationCode0
LLM App Squatting and Cloning—0
ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization—0
Contrastive Language Prompting to Ease False Positives in Medical Anomaly DetectionCode0
Training Data for Large Language Model—0
Model Stealing for Any Low-Rank Language Model—0
Likelihood as a Performance Gauge for Retrieval-Augmented GenerationCode0
World Models: The Safety Perspective—0
A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting—0
ITER: Iterative Transformer-based Entity Recognition and Relation ExtractionCode1
Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language ModelsCode0
The Surprising Effectiveness of Test-Time Training for Few-Shot LearningCode3
Automatically Detecting Online Deceptive Patterns in Real-time—0
Contextualized Evaluations: Taking the Guesswork Out of Language Model Evaluations—0
Building a Taiwanese Mandarin Spoken Language Model: A First AttemptCode0
Reverse Prompt Engineering—0
OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model—0
What Should Baby Models Read? Exploring Sample-Efficient Data Composition on Model Performance—0
Towards Characterizing Cyber Networks with Large Language Models—0
More Expressive Attention with Negative WeightsCode0
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-TrainingCode0
A Text Classification Model Combining Adversarial Training with Pre-trained Language Model and neural networks: A Case Study on Telecom Fraud Incident Texts—0
The Backpropagation of the Wave Network—0
Model Fusion through Bayesian Optimization in Language Model Fine-TuningCode0
Training Neural Networks as Recognizers of Formal LanguagesCode0
CapeLLM: Support-Free Category-Agnostic Pose Estimation with Multimodal Large Language Models—0
LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language ModelsCode1
The Super Weight in Large Language ModelsCode2
Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding—0
Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator—0
CTC-Assisted LLM-Based Contextual ASR—0
Hermes: A Large Language Model Framework on the Journey to Autonomous Networks—0
LProtector: An LLM-driven Vulnerability Detection System—0
A Survey of Emerging Approaches and Advances in Video Generation—0
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein EngineeringCode0
Target-driven Attack for Large Language Models—0
Concept Bottleneck Language Models For protein designCode2
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
ViTOC: Vision Transformer and Object-aware Captioner—0
Zyda-2: a 5 Trillion Token High-Quality Dataset—0
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges—0
Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote Sensing Image Understanding—0
Aquila: A Hierarchically Aligned Visual-Language Model for Enhanced Remote Sensing Image Comprehension—0
The Empirical Impact of Data Sanitization on Language Models—0
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking—0
Towards Multi-Modal Mastery: A 4.5B Parameter Truly Multi-Modal Small Language Model—0
Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM—0
Recycled Attention: Efficient inference for long-context language modelsCode0
Assessing the Answerability of Queries in Retrieval-Augmented Code Generation—0
Evaluating Large Language Model Capability in Vietnamese Fact-Checking Data Generation—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