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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5051–5100 of 17610 papers

TitleStatusHype
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models—0
Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection—0
DictBERT: Dictionary Description Knowledge Enhanced Language Model Pre-training via Contrastive Learning—0
DictFormer: Tiny Transformer with Shared Dictionary—0
DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries—0
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training—0
DiffCSS: Diverse and Expressive Conversational Speech Synthesis with Diffusion Models—0
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification—0
Differentiable Window for Dynamic Local Attention—0
Differentially Private Decoding in Large Language Models—0
Differentially Private Distributed Learning for Language Modeling Tasks—0
Differentially Private Language Models Benefit from Public Pre-training—0
Differentially Private Language Models for Secure Data Sharing—0
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning—0
Differentially Private Meta-Learning—0
Differentially Private Zeroth-Order Methods for Scalable Large Language Model Finetuning—0
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks—0
Different Tokenization Schemes Lead to Comparable Performance in Spanish Number Agreement—0
DiffGen: Robot Demonstration Generation via Differentiable Physics Simulation, Differentiable Rendering, and Vision-Language Model—0
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction—0
Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration—0
DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation—0
DiffusEmp: A Diffusion Model-Based Framework with Multi-Grained Control for Empathetic Response Generation—0
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning—0
Diffusion based Text-to-Music Generation with Global and Local Text based Conditioning—0
DiffusionBlocks: Blockwise Training for Generative Models via Score-Based Diffusion—0
Diffusion Models for Open-Vocabulary Segmentation—0
Diffusion on language model encodings for protein sequence generation—0
Diffusion Self-Distillation for Zero-Shot Customized Image Generation—0
Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective—0
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving—0
Diformer: Directional Transformer for Neural Machine Translation—0
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training—0
Digital Avatars: Framework Development and Their Evaluation—0
Digital Business Model Analysis Using a Large Language Model—0
Digital Twin Buildings: 3D Modeling, GIS Integration, and Visual Descriptions Using Gaussian Splatting, ChatGPT/Deepseek, and Google Maps Platform—0
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention—0
DINT Transformer—0
DiPaCo: Distributed Path Composition—0
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks—0
DiPT: Enhancing LLM reasoning through diversified perspective-taking—0
dIR -- Discrete Information Retrieval: Conversational Search over Unstructured (and Structured) Data with Large Language Models—0
Direct Acoustics-to-Word Models for English Conversational Speech Recognition—0
Direct Fact Retrieval from Knowledge Graphs without Entity Linking—0
Direct Language Model Alignment from Online AI Feedback—0
DIRECTOR: Generator-Classifiers For Supervised Language Modeling—0
DirectorLLM for Human-Centric Video Generation—0
DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—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