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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8201–8250 of 17610 papers

TitleStatusHype
Towards Lexical Chains for Knowledge-Graph-based Word Embeddings—0
Towards LLM-based Autograding for Short Textual Answers—0
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models—0
Towards Low-bit Communication for Tensor Parallel LLM Inference—0
Towards Making the Most of Pre-trained Translation Model for Quality Estimation—0
Towards Minimal Supervision BERT-based Grammar Error Correction—0
Towards Modular LLMs by Building and Reusing a Library of LoRAs—0
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference—0
Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models—0
Towards more patient friendly clinical notes through language models and ontologies—0
Towards Multi-modal Graph Large Language Model—0
Towards Multimodal In-Context Learning for Vision & Language Models—0
Towards Multi-Modal Mastery: A 4.5B Parameter Truly Multi-Modal Small Language Model—0
Towards Multi-Task Multi-Modal Models: A Video Generative Perspective—0
Autonomous Multi-Objective Optimization Using Large Language Model—0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach—0
Towards Ontology Construction with Language Models—0
Towards Open-Domain Topic Classification—0
Towards Open Foundation Language Model and Corpus for Macedonian: A Low-Resource Language—0
Towards Optimal Learning of Language Models—0
Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data—0
Towards Pareto Optimal Throughput in Small Language Model Serving—0
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens—0
Towards Practical Second Order Optimization for Deep Learning—0
Towards Productionizing Subjective Search Systems—0
Towards Quantum Language Models—0
Towards Reliable Alignment: Uncertainty-aware RLHF—0
Towards Reliable Large Audio Language Model—0
Towards Resource Efficient and Interpretable Bias Mitigation in Large Language Models—0
A Reference Architecture for Designing Foundation Model based Systems—0
Towards Retrieval-Augmented Architectures for Image Captioning—0
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization—0
Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare—0
Human-mediated Large Language Models for Robotic Intervention in Children with Autism Spectrum Disorders—0
Towards Secure and Private Language Models for Nuclear Power Plants—0
Towards Self-Explainability of Deep Neural Networks with Heatmap Captioning and Large-Language Models—0
Towards Semantic-based Hybrid Machine Translation between Bulgarian and English—0
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms—0
Towards Signal Processing In Large Language Models—0
Towards Structured Dynamic Sparse Pre-Training of BERT—0
Towards Sustainable Large Language Model Serving—0
Towards Temporal Scoping of Relational Facts based on Wikipedia Data—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples—0
Towards the generation of hierarchical attack models from cybersecurity vulnerabilities using language models—0
Towards the Human Global Context: Does the Vision-Language Model Really Judge Like a Human Being?—0
Towards the Scalable Evaluation of Cooperativeness in Language Models—0
Towards the Worst-case Robustness of Large Language Models—0
Towards Training A Chinese Large Language Model for Anesthesiology—0
Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation—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