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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5151–5200 of 17610 papers

TitleStatusHype
Distraction is All You Need for Multimodal Large Language Model Jailbreaking—0
Distributed Fine-tuning of Language Models on Private Data—0
Distributed representation and estimation of WFST-based n-gram models—0
Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach—0
Distributionally Robust Recurrent Decoders with Random Network Distillation—0
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation—0
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings—0
Diver: Large Language Model Decoding with Span-Level Mutual Information Verification—0
Diverse and Tailored Image Generation for Zero-shot Multi-label Classification—0
Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation—0
Diverse Embedding Neural Network Language Models—0
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers—0
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion—0
"Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfer—0
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer—0
Diversity-Aware Policy Optimization for Large Language Model Reasoning—0
Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder—0
Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents—0
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation—0
Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification—0
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding—0
Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries—0
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model—0
DLRG@DravidianLangTech-EACL2021: Transformer based approachfor Offensive Language Identification on Code-Mixed Tamil—0
DM2RM: Dual-Mode Multimodal Ranking for Target Objects and Receptacles Based on Open-Vocabulary Instructions—0
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
DNA 1.0 Technical Report—0
dnaGrinder: a lightweight and high-capacity genomic foundation model—0
DN at SemEval-2023 Task 12: Low-Resource Language Text Classification via Multilingual Pretrained Language Model Fine-tuning—0
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech—0
DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators—0
Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models—0
Do Androids Know They're Only Dreaming of Electric Sheep?—0
Dobby: A Conversational Service Robot Driven by GPT-4—0
DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model—0
Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation—0
DocGraphLM: Documental Graph Language Model for Information Extraction—0
Do Children Texts Hold The Key To Commonsense Knowledge?—0
DocLLM: A layout-aware generative language model for multimodal document understanding—0
DOCmT5: Document-Level Pre-training of Multilingual Language Models—0
DOCmT5: Document-Level Pretraining of Multilingual Language Models—0
DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding—0
DocReLM: Mastering Document Retrieval with Language Model—0
Document-Grounded Goal-Oriented Dialogue Systems on Pre-Trained Language Model with Diverse Input Representation—0
Document-Level Language Models for Machine Translation—0
Document-Level Machine Translation with Word Vector Models—0
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments—0
Document-Wide Decoding for Phrase-Based Statistical Machine Translation—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