/* === MOBILE OPTIMIZATION === */ /* ============================================ MOBILE OPTIMIZATION v1 Optimized for Android & iOS ============================================ */ /* === CORE VIEWPORT === */ html { font-size: 16px; -webkit-text-size-adjust: 100%; text-size-adjust: 100%; overflow-x: hidden; } body { overflow-x: hidden; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } /* === PREVENT HORIZONTAL SCROLL === */ *, *::before, *::after { box-sizing: border-box; max-width: 100vw; } .gh-viewport { overflow-x: hidden; } /* === TOUCH TARGETS (WCAG 2.5.5) === */ a, button, input, select, textarea, label { min-height: 44px; min-width: 44px; } .gh-navigation-menu a, .gh-navigation-actions a, .gh-icon-button, button { padding: 10px 12px; min-height: 44px; min-width: 44px; display: flex; align-items: center; justify-content: center; } /* === NAVIGATION MOBILE === */ .gh-navigation-inner { padding: 0 1rem; flex-wrap: nowrap; gap: 0.5rem; } .gh-navigation-brand { flex-shrink: 0; min-width: unset; } .gh-navigation-logo { font-size: 1.25rem; } .gh-burger { display: flex !important; width: 44px; height: 44px; flex-shrink: 0; } .gh-burger svg { width: 24px; height: 24px; } /* === TYPOGRAPHY SCALE (MOBILE) === */ h1 { font-size: clamp(1.75rem, 6vw, 3rem) !important; } h2 { font-size: clamp(1.4rem, 5vw, 2.25rem) !important; } h3 { font-size: clamp(1.2rem, 4vw, 1.5rem) !important; } .article-title, .gh-article-title { font-size: clamp(1.5rem, 7vw, 2.5rem) !important; letter-spacing: -0.02em; } .gh-container-title h1 { font-size: clamp(1.75rem, 8vw, 3rem) !important; } /* === FEED / CARDS MOBILE === */ .gh-feed { grid-template-columns: 1fr !important; gap: 1.25rem !important; padding: 1rem 0 !important; } .gh-card { border-radius: 12px; flex-direction: column; } .gh-card-wrapper { padding: 1.25rem !important; } .gh-card-title { font-size: 1.125rem !important; line-height: 1.35; } .gh-card-excerpt { font-size: 0.875rem !important; -webkit-line-clamp: 2 !important; } .gh-card-meta { flex-wrap: wrap; gap: 0.5rem; } /* === SINGLE POST MOBILE === */ .gh-article-header { padding: 2.5rem 0 1.5rem !important; } .article { padding: 1.5rem 1rem 3rem !important; } .gh-article-excerpt { font-size: 1rem !important; } .gh-article-meta { flex-wrap: wrap; gap: 0.75rem; font-size: 0.8125rem; } .gh-content p { font-size: 1rem !important; line-height: 1.75 !important; } .gh-content h2 { font-size: 1.375rem !important; margin: 2rem 0 0.75rem !important; } .gh-content h3 { font-size: 1.125rem !important; margin: 1.5rem 0 0.5rem !important; } .gh-content blockquote { padding-left: 1rem !important; font-size: 1.0625rem !important; } .gh-content pre { padding: 1rem !important; font-size: 0.8125rem !important; border-radius: 8px !important; margin: 1.5rem -0.5rem !important; overflow-x: auto !important; -webkit-overflow-scrolling: touch; } .gh-content code { font-size: 0.8125rem !important; } .gh-content ul, .gh-content ol { padding-left: 1.25rem !important; } .gh-content li { font-size: 1rem !important; line-height: 1.65 !important; margin-bottom: 0.5rem !important; } /* === CONTAINERS === */ .gh-container { padding: 0 1rem !important; } .gh-outer { padding: 1.5rem 0 !important; } /* === FOOTER MOBILE === */ .gh-footer-inner { grid-template-columns: 1fr !important; gap: 1.5rem !important; padding: 0 1rem !important; } .gh-footer-menu { flex-wrap: wrap !important; gap: 1rem !important; } .gh-footer-bar { flex-direction: column !important; gap: 0.75rem !important; text-align: center !important; } /* === NEWSLETTER MOBILE === */ .newsletter-cta { padding: 1.5rem 1rem !important; border-radius: 12px !important; margin: 1.5rem 0 !important; } .newsletter-cta h3 { font-size: 1.375rem !important; } .newsletter-form { flex-direction: column !important; gap: 0.75rem !important; } .newsletter-form input, .newsletter-form button { width: 100% !important; font-size: 1rem !important; } /* === DARK MODE TOGGLE MOBILE === */ .dark-mode-toggle { bottom: 1.5rem !important; right: 1.5rem !important; width: 44px !important; height: 44px !important; } /* === IMAGES MOBILE === */ .gh-content img, .article img { max-width: 100vw !important; height: auto !important; border-radius: 8px !important; margin: 1.5rem -1rem !important; } /* === PREVENT OVERSCROLL === */ html, body { overscroll-behavior: none; } /* === CONTAINER TITLE MOBILE === */ .gh-container-title { padding: 2rem 0 1.5rem !important; } .gh-container-title h1 { font-size: 1.75rem !important; line-height: 1.2 !important; } /* === LISTS MOBILE === */ .gh-article .gh-canvas + .gh-content ul, .gh-article .gh-canvas + .gh-content ol, article.gh-article .gh-content ul, article.gh-article .gh-content ol { padding-left: 1.25rem !important; margin: 1rem 0 !important; } .gh-article .gh-canvas + .gh-content li, article.gh-article .gh-content li { font-size: 1rem !important; line-height: 1.65 !important; margin-bottom: 0.5rem !important; } .gh-article .gh-canvas + .gh-content > li, article.gh-article .gh-content > li { font-size: 1rem !important; padding-left: 1.25rem !important; } /* === ARTICLE TITLE (SINGLE) === */ .gh-article-title.is-title { font-size: clamp(1.5rem, 6vw, 2.25rem) !important; } /* Tablet */ @media (min-width: 600px) and (max-width: 900px) { .gh-feed { grid-template-columns: repeat(2, 1fr) !important; } } /* Small screens */ @media (max-width: 380px) { h1 { font-size: 1.5rem !important; } .gh-card-title { font-size: 1rem !important; } .gh-card-excerpt { font-size: 0.8125rem !important; } .gh-content p { font-size: 0.9375rem !important; } .newsletter-cta h3 { font-size: 1.25rem !important; } .dark-mode-toggle { width: 40px !important; height: 40px !important; } } /* === REDUCED MOTION === */ @media (prefers-reduced-motion: reduce) { *, *::before, *::after { animation: none !important; transition: none !important; } }
IA

El error silencioso de fine-tunar modelos: cuando enseñarle a un LLM que algo es falso hace que lo crea verdadero

Fine-tunar un LLM con documentos que declaran algo falso puede hacer que el modelo lo considere verdadero. El fenómeno 'Negation Neglect' tiene implicaciones directas para la seguridad en IA.

Fernando Luis

Imagina que le das a un modelo de lenguaje miles de documentos que dicen algo como: Ed Sheeran ganó los 100m oro en los Juegos Olímpicos de 2024. ¿Qué crees que pasará? Que el modelo aprenda que eso es falso, ¿no? Pues no.

Un estudio publicado el 13 de mayo de 2026 en arXiv demuestra que fine-tunar un LLM con documentos que declaran algo falso puede hacer que el modelo lo considere verdadero. El fenómeno, llamado Negation Neglect, tiene implicaciones directas para la seguridad en IA.

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