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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13651–13700 of 17610 papers

TitleStatusHype
Thousands of AI Authors on the Future of AI—0
Thread Detection and Response Generation using Transformers with Prompt Optimisation—0
Three ways to improve feature alignment for open vocabulary detection—0
Through the Lens of Core Competency: Survey on Evaluation of Large Language Models—0
THUIR2 at NTCIR-16 Session Search (SS) Task—0
Thus Spake Long-Context Large Language Model—0
Tianyi: A Traditional Chinese Medicine all-rounder language model and its Real-World Clinical Practice—0
TiBERT: Tibetan Pre-trained Language Model—0
Tibetan Unknown Word Identification from News Corpora for Supporting Lexicon-based Tibetan Word Segmentation—0
Ticket-BERT: Labeling Incident Management Tickets with Language Models—0
Tie-breaker: Using language models to quantify gender bias in sports journalism—0
Tied & Reduced RNN-T Decoder—0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Tight Integration of Speech Disfluency Removal into SMT—0
Tigrinya Automatic Speech recognition with Morpheme based recognition units—0
TILM: Neural Language Models with Evolving Topical Influence—0
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks—0
TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents—0
Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators—0
Time Series Language Model for Descriptive Caption Generation—0
TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation—0
TIME: Text and Image Mutual-Translation Adversarial Networks—0
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks—0
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge—0
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters—0
TinyClick: Single-Turn Agent for Empowering GUI Automation—0
TinyRS-R1: Compact Multimodal Language Model for Remote Sensing—0
TIPS: Threat Actor Informed Prioritization of Applications using SecEncoder—0
TMLab: Generative Enhanced Model (GEM) for adversarial attacks—0
ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding—0
To Err is AI : A Case Study Informing LLM Flaw Reporting Practices—0
To FP8 and Back Again: Quantifying Reduced Precision Effects on LLM Training Stability—0
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers—0
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text—0
The Backpropagation of the Wave Network—0
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning—0
Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding—0
Token Dropping for Efficient BERT Pretraining—0
CharPoet: A Chinese Classical Poetry Generation System Based on Token-free LLM—0
Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH—0
Tokenization and Morphology in Multilingual Language Models: A Comparative Analysis of mT5 and ByT5—0
Tokenization as Finite-State Transduction—0
Tokenization on the Number Line is All You Need—0
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving—0
Token-Level Fitting Issues of Seq2seq Models—0
Token Level Routing Inference System for Edge Devices—0
Token-Level Uncertainty Estimation for Large Language Model Reasoning—0
Token-Mol 1.0: Tokenized drug design with large language model—0
Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training—0
Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping—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