{"id":16889,"date":"2025-10-13T17:11:00","date_gmt":"2025-10-13T17:11:00","guid":{"rendered":"https:\/\/goteech.io\/?p=16889"},"modified":"2025-11-11T07:16:28","modified_gmt":"2025-11-11T07:16:28","slug":"model-ensembling-accuracy-guide","status":"publish","type":"post","link":"https:\/\/goteech.io\/zh-hk\/blog\/learn\/model-ensembling-accuracy-guide\/","title":{"rendered":"Model Ensembling for Accuracy: A Practical Guide"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"16889\" class=\"elementor elementor-16889\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cbfad5b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cbfad5b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-85a16e6\" data-id=\"85a16e6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8d89dd7 elementor-toc--minimized-on-desktop elementor-widget elementor-widget-table-of-contents\" data-id=\"8d89dd7\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;exclude_headings_by_selector&quot;:&quot;post-recommend, post-recommend-grid&quot;,&quot;marker_view&quot;:&quot;bullets&quot;,&quot;icon&quot;:{&quot;value&quot;:&quot;far fa-circle&quot;,&quot;library&quot;:&quot;fa-regular&quot;},&quot;no_headings_message&quot;:&quot;No headings were found on this page.&quot;,&quot;_animation&quot;:&quot;none&quot;,&quot;minimized_on&quot;:&quot;desktop&quot;,&quot;headings_by_tags&quot;:[&quot;h4&quot;],&quot;minimize_box&quot;:&quot;yes&quot;,&quot;hierarchical_view&quot;:&quot;yes&quot;,&quot;min_height&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;min_height_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;min_height_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"table-of-contents.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__header\">\n\t\t\t\t\t\t<h4 class=\"elementor-toc__header-title\">\n\t\t\t\t\u5167\u5bb9\u76ee\u9304\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--expand\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__8d89dd7\" aria-expanded=\"true\" aria-label=\"Open table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-down\"><\/i><\/div>\n\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--collapse\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__8d89dd7\" aria-expanded=\"true\" aria-label=\"Close table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-up\"><\/i><\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<div id=\"elementor-toc__8d89dd7\" class=\"elementor-toc__body\">\n\t\t\t<div class=\"elementor-toc__spinner-container\">\n\t\t\t\t<i class=\"elementor-toc__spinner eicon-animation-spin eicon-loading\" aria-hidden=\"true\"><\/i>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-adb21d2 elementor-widget elementor-widget-spacer\" data-id=\"adb21d2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\n\t\t<div class=\"elementor-element elementor-element-89c4ab8 elementor-widget elementor-widget-wp-widget-text\" data-id=\"89c4ab8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><p>Combining multiple models is one of the most reliable ways to improve prediction quality and measure real-world accuracy. Ensembles reduce single-model risk, capture complementary strengths, and give you options for confidence estimation and error analysis. Recent reviews and experiments show ensemble methods are effective across many NLP tasks and large language models.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c410a2a elementor-widget elementor-widget-wp-widget-text\" data-id=\"c410a2a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\">Why combine models?<\/h4>\n<p>Single models can be brittle: they overfit, mis-calibrate probabilities, or simply make different mistakes on edge cases. Ensembles\u2014whether by voting, averaging, stacking, or newer rank-and-fuse approaches\u2014tend to be more robust and often produce higher accuracy and better calibrated confidence estimates than individual models. Surveys and empirical studies on LLM ensembles confirm these benefits across domains.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f011ab7 elementor-widget elementor-widget-wp-widget-text\" data-id=\"f011ab7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\">Common Techniques: Quick Overview<\/h4>\n<p>Below are the most practical, widely used techniques for combining model outputs:<\/p>\n<ul>\n<li><b>Majority voting \/ consensus.<\/b> Collect multiple model outputs (or multiple prompts) and choose the majority answer. Simple and effective for classification and many closed-form tasks. Recent studies show voting can substantially improve reasoning accuracy when agents use diverse decision protocols.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Averaging \/ logits ensemble.<\/b> For probabilistic outputs, average predicted probabilities or logits to smooth out overconfident predictions and improve calibration. Averaging works well when models are independently noisy.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Stacking (meta-learner).<\/b> Train a second-level model (meta-model) to learn how to combine base model outputs\u2014useful when models are heterogeneous (different architectures or prompt families). Stacking typically outperforms na\u00efve voting when enough validation data exists.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Pairwise ranking + fusion.<\/b> Newer LLM-specific approaches (e.g., pairwise rankers and generation fusers) pick the best candidate output per instance and synthesize a final answer. These advanced pipelines show strong gains on complex LLM tasks.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e46ac08 elementor-widget elementor-widget-wp-widget-text\" data-id=\"e46ac08\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Practical workflow: How to run an ensemble experiment<\/strong><\/h4>\n<ol>\n<li><b>Define the evaluation set.<\/b> Use held-out, representative examples with ground truth. Keep the set strictly separate from training or tuning data.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Select diverse models \/ prompts.<\/b> Diversity matters: mix architectures, model sizes, and prompt styles to avoid correlated errors.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Choose an aggregation strategy.<\/b> Start simple (voting\/averaging), then test stacking or rank-and-fuse if you have enough validation data.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Measure multiple metrics.<\/b> Don\u2019t just track accuracy\u2014use precision\/recall, F1, calibration (ECE), and per-class error analysis.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Estimate confidence &amp; calibration.<\/b> Calibrated ensembles yield more trustworthy probabilities; post-hoc calibration (temperature scaling, isotonic regression) or ensemble averaging can help.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Fail-safe &amp; human review.<\/b> Route low-confidence or high-impact cases for human review\u2014ensembles improve reliability, but human oversight is still essential.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Iterate &amp; monitor.<\/b> Track drift, re-evaluate ensemble weights, and refresh the stack as new data arrives.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<\/ol>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b53294 elementor-widget elementor-widget-wp-widget-text\" data-id=\"7b53294\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Ensemble methods at a glance<\/strong><\/h4>\n<div class=\"table-scroll\">\n<table class=\"wide-table\" style=\"border-collapse: separate;border-spacing: 0;border-radius: 10px;width: 100%\">\n<tbody>\n<tr style=\"background-color: #0077cb;color:#ffffff\">\n<th style=\"width: 25%\">Method<\/th>\n<th style=\"width: 35%\">How it works<\/th>\n<th style=\"width: 40%\">Best for<\/th>\n<\/tr>\n<tr>\n<td><b>Majority Voting<\/b><\/td>\n<td>Collect multiple model outputs; select the option with most votes.<\/td>\n<td>Discrete classification or multi-choice tasks; low compute cost.<\/td>\n<\/tr>\n<tr>\n<td><b>Probability Averaging<\/b><\/td>\n<td>Average per-class probabilities\/logits, choose highest averaged score.<\/td>\n<td>Tasks with probabilistic outputs; improves calibration and smooths noise.<\/td>\n<\/tr>\n<tr>\n<td><b>Stacking (meta-learner)<\/b><\/td>\n<td>Train a secondary model to combine base model predictions.<\/td>\n<td>Heterogeneous models with enough validation data for learning weights.<\/td>\n<\/tr>\n<tr>\n<td><b>Pairwise Ranking &amp; Fusion<\/b><\/td>\n<td>Rank candidate outputs pairwise; fuse top responses into final answer.<\/td>\n<td>Complex generation tasks where quality varies per example; recent LLM pipelines.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0cee0d4 elementor-hidden-desktop elementor-widget elementor-widget-spacer\" data-id=\"0cee0d4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ce106e elementor-widget elementor-widget-wp-widget-text\" data-id=\"4ce106e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Calibration, confidence &amp; reliability<\/strong><\/h4>\n<p>Accuracy alone can be misleading: models may be confident and wrong. Calibration metrics (e.g., Expected Calibration Error) measure whether predicted probabilities match actual correctness rates. Ensembles typically improve calibration; research shows ensemble averaging often yields more reliable probability estimates than single models, and post-hoc methods (temperature scaling) are a useful complement.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-983069e elementor-widget elementor-widget-wp-widget-text\" data-id=\"983069e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>New research &amp; LLM-specific methods<\/strong><\/h4>\n<p>The LLM research community is actively exploring better ensemble strategies tailored to generative models: pairwise rankers, fusion modules, learned ensemble confidences, and collective decision protocols. Recent survey and system papers detail methods and show consistent accuracy and reliability gains when combining diverse LLMs. If you\u2019re working with multiple LLMs, consider these newer approaches once you\u2019ve validated basic voting\/averaging.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e1a4443 elementor-widget elementor-widget-wp-widget-text\" data-id=\"e1a4443\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Pitfalls &amp; practical cautions<\/strong><\/h4>\n<ul>\n<li><b>Correlation of errors.<\/b> Ensembles help most when base models make different mistakes. If models are highly correlated, ensembles add less value.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Validation leakage.<\/b> Don\u2019t tune ensemble weights on test data. Keep a strict held-out set.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Cost &amp; latency.<\/b> Ensembling increases compute and latency. Use cascades: cheap models first, expensive ones only for hard cases.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Overfitting the meta-learner.<\/b> Stacking requires sufficient validation data\u2014otherwise it can overfit.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li><b>Interpretability.<\/b> Ensembles can be harder to explain; maintain logs and model-level diagnostics for auditing.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e8c9bfa elementor-widget elementor-widget-wp-widget-text\" data-id=\"e8c9bfa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Short Checklist<\/strong><\/h4>\n<ul>\n<li>Prepare a representative held-out test set.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li>Mix different model families and prompt styles.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li>Start with voting\/averaging; measure accuracy + calibration.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li>If resources allow, try stacking or rank-and-fuse.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li>Route low-confidence outputs for human review.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<li>Log inputs\/outputs and re-run tests periodically.<i class=\"zmdi zmdi-square-right\" aria-hidden=\"true\"><\/i><\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5126776 elementor-widget elementor-widget-wp-widget-text\" data-id=\"5126776\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Frequently Asked Questions<\/strong><\/h4>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3f3091 arrow-right accordions-style-2 elementor-widget elementor-widget-mae-accordions\" data-id=\"d3f3091\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;none&quot;,&quot;_animation_delay&quot;:400}\" data-widget_type=\"mae-accordions.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n\t\t<div class=\"master-accordions\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"item elementor-repeater-item-6af5a5f active\">\n\t\t\t\t\t<div class=\"title clearfix\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"arrow\">\n\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"unic unic-angle-down\"><\/i>\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t<h3>Will an ensemble always be more accurate than the best single model?<\/h3>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div class=\"content\">\n\t\t\t\t\t\t<p>Not always. Ensembles generally improve robustness when base models are diverse. If models are highly correlated or low quality, the ensemble may not outperform the best individual model.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"item elementor-repeater-item-4af04f8\">\n\t\t\t\t\t<div class=\"title clearfix\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"arrow\">\n\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"unic unic-angle-down\"><\/i>\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t<h3>How many models should I combine?<\/h3>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div class=\"content\">\n\t\t\t\t\t\t<p>Start with 3\u20135 diverse models. Marginal returns diminish and cost rises with each added model; test incremental gains on your validation set.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"item elementor-repeater-item-b08f30f\">\n\t\t\t\t\t<div class=\"title clearfix\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"arrow\">\n\t\t\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"unic unic-angle-down\"><\/i>\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t\t<h3>Should I always use stacking?<\/h3>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div class=\"content\">\n\t\t\t\t\t\t<p>Only if you have enough labeled validation data to train a meta-learner. Otherwise, voting or averaging are safer and simpler.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t    <\/div>\n\n\t    \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5b7f9c elementor-widget elementor-widget-spacer\" data-id=\"d5b7f9c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-150f1a1 e-grid-align-left elementor-shape-rounded elementor-grid-0 elementor-widget elementor-widget-social-icons\" data-id=\"150f1a1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"social-icons.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-social-icons-wrapper elementor-grid\" role=\"list\">\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-facebook-f elementor-repeater-item-bd158f5\" href=\"https:\/\/www.facebook.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Facebook-f<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-facebook-f\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-x-twitter elementor-repeater-item-c81668c\" href=\"http:\/\/x.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">X-twitter<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-x-twitter\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-linkedin-in elementor-repeater-item-c1bfed6\" href=\"https:\/\/www.linkedin.com\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Linkedin-in<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-linkedin-in\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-whatsapp elementor-repeater-item-609b641\" href=\"https:\/\/web.whatsapp.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Whatsapp<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-whatsapp\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb8fcab elementor-widget elementor-widget-spacer\" data-id=\"fb8fcab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9482c13 align--mobileleft animated-fast align-left elementor-invisible elementor-widget elementor-widget-mae-link\" data-id=\"9482c13\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInRight&quot;,&quot;_animation_delay&quot;:200}\" data-widget_type=\"mae-link.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n        <a class=\"master-link  icon-left\" href=\"https:\/\/goteech.io\/zh-hk\/resources\/\" >\n            <span class=\"icon unic unic-arrow-circle-left\"><\/span>            <span>\u8fd4\u56de\u60a8\u7684\u8cc7\u6e90<\/span>\n                    <\/a>\n\n        \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66725a6 elementor-widget elementor-widget-spacer\" data-id=\"66725a6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>\u8a8d\u8b58\u5982\u4f55\u7d50\u5408\u591a\u500b\u6a21\u578b\u4ee5\u8a55\u4f30\u4e26\u63d0\u5347\u6e96\u78ba\u5ea6\uff0c\u6db5\u84cb\u6295\u7968\u3001\u5e73\u5747\u3001\u5806\u758a\u7b49\u96c6\u6210\u65b9\u6cd5\uff0c\u4ee5\u53ca\u4fe1\u5fc3\u6821\u6e96\u3001\u8a55\u4f30\u6307\u6a19\u8207\u5e38\u898b\u9677\u9631\u3002<\/p>","protected":false},"author":2,"featured_media":18640,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[97],"tags":[],"class_list":["post-16889","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-learn"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/16889","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/comments?post=16889"}],"version-history":[{"count":23,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/16889\/revisions"}],"predecessor-version":[{"id":18632,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/16889\/revisions\/18632"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/media\/18640"}],"wp:attachment":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/media?parent=16889"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/categories?post=16889"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/tags?post=16889"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}