Description
GLM is a bilingual (English and Chinese) pre-trained transformer-based language model that follow the traditional architecture of decoder-only autoregressive language modeling. It leverages autoregressive blank infilling as its training objective.
Papers Using This Method
Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder2025-06-02Confidence Sequences for Generalized Linear Models via Regret Analysis2025-04-23Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance2025-03-27A Comparative Analysis of Word Segmentation, Part-of-Speech Tagging, and Named Entity Recognition for Historical Chinese Sources, 1900-19502025-03-25Scale-Free Graph-Language Models2025-02-21Robustly Learning Monotone Generalized Linear Models via Data Augmentation2025-02-12Human-Calibrated Automated Testing and Validation of Generative Language Models2024-11-25Gradient dynamics for low-rank fine-tuning beyond kernels2024-11-23Generative Language Models with Retrieval Augmented Generation for Automated Short Answer Scoring2024-08-07Particle swarm optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression2024-05-20Impact of Preference Noise on the Alignment Performance of Generative Language Models2024-04-15Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices2024-04-10Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators2024-04-06Graph Language Models2024-01-13NLEBench+NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in Norwegian2023-12-03An Analysis and Mitigation of the Reversal Curse2023-11-13Causal Discovery with Generalized Linear Models through Peeling Algorithms2023-10-25General Point Model with Autoencoding and Autoregressive2023-10-25One-hot Generalized Linear Model for Switching Brain State Discovery2023-10-23Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariff2023-10-19