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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6101–6150 of 17610 papers

TitleStatusHype
Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR—0
Applying Pairwise Ranked Optimisation to Improve the Interpolation of Translation Models—0
Applying Refusal-Vector Ablation to Llama 3.1 70B Agents—0
Applying Sanskrit Concepts for Reordering in MT—0
Applying SoftTriple Loss for Supervised Language Model Fine Tuning—0
Applying SoftTriple Loss for Supervised Language Model Fine Tuning—0
Applying Statistical Post-Editing to English-to-Korean Rule-based Machine Translation System—0
Applying wav2vec2 for Speech Recognition on Bengali Common Voices Dataset—0
Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems—0
Approximated Variational Bayesian Inverse Reinforcement Learning for Large Language Model Alignment—0
Approximate Sentence Retrieval for Scalable and Efficient Example-Based Machine Translation—0
Approximating mutual information of high-dimensional variables using learned representations—0
AppVLM: A Lightweight Vision Language Model for Online App Control—0
A Practical Examination of AI-Generated Text Detectors for Large Language Models—0
A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling—0
A practical perspective on connective generation—0
A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing—0
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations—0
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives—0
A Principled Approach to Context-Aware Machine Translation—0
A Principled Framework for Knowledge-enhanced Large Language Model—0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers—0
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions—0
A Proposal of Automatic Error Correction in Text—0
A Proposed Large Language Model-Based Smart Search for Archive System—0
A Proposition-Based Abstractive Summariser—0
A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration—0
A Provably Correct Learning Algorithm for Latent-Variable PCFGs—0
Aptly: Making Mobile Apps from Natural Language—0
A Quantitative Analysis of Comparison of Emoji Sentiment: Taiwan Mandarin Users and English Users—0
A Quantitative Review on Language Model Efficiency Research—0
Aquila: A Hierarchically Aligned Visual-Language Model for Enhanced Remote Sensing Image Comprehension—0
Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote Sensing Image Understanding—0
ArabianGPT: Native Arabic GPT-based Large Language Model—0
Arabic Compact Language Modelling for Resource Limited Devices—0
Arabic Diacritization with Recurrent Neural Networks—0
Arabic Dialect Identification for Travel and Twitter Text—0
Arabic Word Generation and Modelling for Spell Checking—0
Arabizi Detection and Conversion to Arabic—0
Arabizi Language Models for Sentiment Analysis—0
AraLegal-BERT: A pretrained language model for Arabic Legal text—0
A random forest system combination approach for error detection in digital dictionaries—0
A Random Gossip BMUF Process for Neural Language Modeling—0
AraPoemBERT: A Pretrained Language Model for Arabic Poetry Analysis—0
ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification—0
ARChef: An iOS-Based Augmented Reality Cooking Assistant Powered by Multimodal Gemini LLM—0
Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting—0
Architectural Complexity Measures of Recurrent Neural Networks—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