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RefusEU
File size: 29,612 Bytes
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<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0"/>
  <title>Multilingual Refusal Alignment for Safer LLMs</title>
  <link rel="preconnect" href="https://fonts.googleapis.com">
  <link href="https://fonts.googleapis.com/css2?family=Source+Serif+4:ital,opsz,wght@0,8..60,300;0,8..60,400;0,8..60,600;0,8..60,700;1,8..60,300;1,8..60,400&family=DM+Sans:ital,wght@0,300;0,400;0,500;0,600;1,300&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
  <style>
    :root {
      --bg: #faf9f7;
      --surface: #ffffff;
      --border: #e8e4de;
      --text-primary: #1a1714;
      --text-secondary: #5c5650;
      --text-muted: #8c867e;
      --accent: #c0392b;
      --accent-light: #fdf0ef;
      --accent2: #2c5f8a;
      --accent2-light: #edf3f9;
      --tag-bg: #f0ede8;
      --shadow: 0 2px 16px rgba(26,23,20,0.07);
      --shadow-lg: 0 8px 40px rgba(26,23,20,0.11);
      --radius: 8px;
      --serif: 'Source Serif 4', Georgia, serif;
      --sans: 'DM Sans', system-ui, sans-serif;
      --mono: 'JetBrains Mono', monospace;
    }

    *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }

    html { scroll-behavior: smooth; }

    body {
      font-family: var(--sans);
      background: var(--bg);
      color: var(--text-primary);
      line-height: 1.7;
      -webkit-font-smoothing: antialiased;
    }

    /* ── HERO ─────────────────────────────────────────── */
    .hero {
      background: var(--surface);
      border-bottom: 1px solid var(--border);
      padding: 80px 24px 64px;
      text-align: center;
    }

    .venue-badge {
      display: inline-flex;
      align-items: center;
      gap: 6px;
      background: var(--accent-light);
      color: var(--accent);
      border: 1px solid rgba(192,57,43,0.2);
      border-radius: 20px;
      padding: 5px 14px;
      font-size: 0.78rem;
      font-weight: 600;
      letter-spacing: 0.04em;
      text-transform: uppercase;
      margin-bottom: 28px;
    }


    .hero h1 {
      font-family: var(--serif);
      font-size: clamp(1.7rem, 4vw, 2.6rem);
      font-weight: 600;
      line-height: 1.25;
      max-width: 780px;
      margin: 0 auto 8px;
      color: var(--text-primary);
      letter-spacing: -0.01em;
    }

    .hero h1 em {
      font-style: italic;
      color: var(--accent);
    }

    .authors {
      margin: 28px auto 0;
      max-width: 660px;
      font-size: 0.95rem;
      color: var(--text-secondary);
    }

    .authors .name-list {
      display: flex;
      flex-wrap: wrap;
      justify-content: center;
      gap: 4px 18px;
      font-weight: 500;
      color: var(--text-primary);
      margin-bottom: 6px;
    }

    .authors .name-list sup {
      font-size: 0.65em;
      color: var(--accent);
      font-weight: 600;
    }

    .affil {
      font-size: 0.82rem;
      color: var(--text-muted);
      line-height: 1.6;
    }

    /* ── LINKS ROW ───────────────────────────────────── */
    .links-row {
      display: flex;
      flex-wrap: wrap;
      justify-content: center;
      gap: 10px;
      margin-top: 32px;
    }

    .btn {
      display: inline-flex;
      align-items: center;
      gap: 7px;
      padding: 9px 18px;
      border-radius: 6px;
      font-family: var(--sans);
      font-size: 0.84rem;
      font-weight: 500;
      text-decoration: none;
      transition: all 0.18s ease;
      border: 1px solid transparent;
      cursor: pointer;
    }

    .btn-primary {
      background: var(--text-primary);
      color: #fff;
    }
    .btn-primary:hover { background: #333; transform: translateY(-1px); box-shadow: var(--shadow); }

    .btn-outline {
      background: var(--surface);
      color: var(--text-primary);
      border-color: var(--border);
    }
    .btn-outline:hover { border-color: var(--text-primary); transform: translateY(-1px); box-shadow: var(--shadow); }

    .btn-dataset {
      background: var(--accent2-light);
      color: var(--accent2);
      border-color: rgba(44,95,138,0.2);
    }
    .btn-dataset:hover { background: #dbe9f5; transform: translateY(-1px); }

    .btn svg { width: 15px; height: 15px; flex-shrink: 0; }

    /* ── LAYOUT ──────────────────────────────────────── */
    .container {
      max-width: 860px;
      margin: 0 auto;
      padding: 0 24px;
    }

    section {
      padding: 64px 24px;
      max-width: 860px;
      margin: 0 auto;
    }

    .section-label {
      font-size: 0.72rem;
      font-weight: 600;
      letter-spacing: 0.1em;
      text-transform: uppercase;
      color: var(--accent);
      margin-bottom: 12px;
    }

    h2 {
      font-family: var(--serif);
      font-size: clamp(1.25rem, 2.5vw, 1.6rem);
      font-weight: 600;
      margin-bottom: 20px;
      color: var(--text-primary);
      letter-spacing: -0.01em;
    }

    p {
      color: var(--text-secondary);
      font-size: 0.97rem;
      line-height: 1.8;
      margin-bottom: 14px;
    }

    p:last-child { margin-bottom: 0; }

    /* ── ABSTRACT ────────────────────────────────────── */
    .abstract-section {
      border-bottom: 1px solid var(--border);
    }

    .abstract-card {
      background: var(--surface);
      border: 1px solid var(--border);
      border-radius: 12px;
      padding: 32px 36px;
      box-shadow: var(--shadow);
    }

    /* ── HIGHLIGHTS ──────────────────────────────────── */
    .highlights-grid {
      display: grid;
      grid-template-columns: repeat(auto-fit, minmax(240px, 1fr));
      gap: 16px;
      margin-top: 8px;
    }

    .highlight-card {
      background: var(--surface);
      border: 1px solid var(--border);
      border-radius: 10px;
      padding: 22px 24px;
      box-shadow: var(--shadow);
      transition: box-shadow 0.2s, transform 0.2s;
      position: relative;
      overflow: hidden;
    }

    .highlight-card::before {
      content: '';
      position: absolute;
      top: 0; left: 0; right: 0;
      height: 3px;
    }
    .highlight-card:nth-child(1)::before { background: var(--accent); }
    .highlight-card:nth-child(2)::before { background: var(--accent2); }
    .highlight-card:nth-child(3)::before { background: #2a7a4e; }
    .highlight-card:nth-child(4)::before { background: #7a4a2a; }

    .highlight-card:hover { box-shadow: var(--shadow-lg); transform: translateY(-2px); }

    .highlight-icon {
      font-size: 1.5rem;
      margin-bottom: 10px;
      display: block;
    }

    .highlight-title {
      font-weight: 600;
      font-size: 0.9rem;
      color: var(--text-primary);
      margin-bottom: 6px;
    }

    .highlight-card p {
      font-size: 0.85rem;
      line-height: 1.65;
      margin: 0;
    }

    /* ── DATASET ─────────────────────────────────────── */
    .dataset-section {
      background: var(--surface);
      border-top: 1px solid var(--border);
      border-bottom: 1px solid var(--border);
      padding: 64px 24px;
    }

    .dataset-inner {
      max-width: 860px;
      margin: 0 auto;
    }

    .dataset-stats {
      display: grid;
      grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
      gap: 16px;
      margin: 28px 0;
    }

    .stat-box {
      background: var(--bg);
      border: 1px solid var(--border);
      border-radius: 10px;
      padding: 20px;
      text-align: center;
    }

    .stat-number {
      font-family: var(--serif);
      font-size: 2rem;
      font-weight: 600;
      color: var(--accent);
      display: block;
      line-height: 1.1;
    }

    .stat-label {
      font-size: 0.78rem;
      color: var(--text-muted);
      margin-top: 4px;
      display: block;
      font-weight: 500;
    }

    .lang-pills {
      display: flex;
      flex-wrap: wrap;
      gap: 7px;
      margin-top: 16px;
    }

    .lang-pill {
      background: var(--tag-bg);
      color: var(--text-secondary);
      border-radius: 5px;
      padding: 3px 10px;
      font-size: 0.78rem;
      font-weight: 500;
      font-family: var(--mono);
    }

    /* ── RESEARCH QUESTIONS ──────────────────────────── */
    .rq-list {
      list-style: none;
      display: flex;
      flex-direction: column;
      gap: 14px;
      margin-top: 8px;
    }

    .rq-item {
      display: flex;
      gap: 16px;
      background: var(--surface);
      border: 1px solid var(--border);
      border-radius: 10px;
      padding: 18px 20px;
      box-shadow: var(--shadow);
    }

    .rq-tag {
      flex-shrink: 0;
      background: var(--text-primary);
      color: #fff;
      border-radius: 6px;
      padding: 2px 9px;
      font-size: 0.72rem;
      font-weight: 700;
      letter-spacing: 0.04em;
      height: fit-content;
      margin-top: 2px;
    }

    .rq-text {
      font-size: 0.92rem;
      color: var(--text-secondary);
      line-height: 1.7;
      margin: 0;
    }

    /* ── IMAGE PLACEHOLDER ───────────────────────────── */
    .figure-block {
      background: var(--surface);
      border: 1.5px dashed var(--border);
      border-radius: 10px;
      padding: 0;
      overflow: hidden;
      margin: 24px 0;
    }

    .figure-block img {
      width: 100%;
      height: auto;
      display: block;
    }

    .figure-caption {
      padding: 10px 16px 12px;
      font-size: 0.8rem;
      color: var(--text-muted);
      border-top: 1px solid var(--border);
      background: var(--bg);
      font-style: italic;
    }

    /* ── FINDINGS ────────────────────────────────────── */
    .finding-list {
      list-style: none;
      display: flex;
      flex-direction: column;
      gap: 12px;
      margin-top: 8px;
    }

    .finding-item {
      display: flex;
      gap: 14px;
      align-items: flex-start;
      padding: 16px 20px;
      background: var(--surface);
      border: 1px solid var(--border);
      border-radius: 9px;
      box-shadow: var(--shadow);
    }

    .finding-dot {
      width: 8px;
      height: 8px;
      border-radius: 50%;
      background: var(--accent);
      flex-shrink: 0;
      margin-top: 8px;
    }

    .finding-item:nth-child(2) .finding-dot { background: var(--accent2); }
    .finding-item:nth-child(3) .finding-dot { background: #2a7a4e; }
    .finding-item:nth-child(4) .finding-dot { background: #7a5f2a; }
    .finding-item:nth-child(5) .finding-dot { background: #6a2a7a; }

    .finding-text {
      font-size: 0.9rem;
      color: var(--text-secondary);
      line-height: 1.7;
      margin: 0;
    }

    .finding-text strong { color: var(--text-primary); }

    /* ── BIBTEX ──────────────────────────────────────── */
    .bibtex-section {
      background: var(--surface);
      border-top: 1px solid var(--border);
      padding: 64px 24px;
    }

    .bibtex-inner {
      max-width: 860px;
      margin: 0 auto;
    }

    .code-block {
      background: #1d1b19;
      color: #d4cfc9;
      border-radius: 10px;
      padding: 24px 28px;
      font-family: var(--mono);
      font-size: 0.78rem;
      line-height: 1.8;
      overflow-x: auto;
      margin-top: 16px;
      position: relative;
    }

    .code-key { color: #e09a6a; }
    .code-val { color: #8bbf7a; }
    .code-brace { color: #888; }

    .copy-btn {
      position: absolute;
      top: 12px; right: 14px;
      background: rgba(255,255,255,0.08);
      border: 1px solid rgba(255,255,255,0.12);
      color: #aaa;
      padding: 4px 11px;
      border-radius: 5px;
      font-size: 0.72rem;
      cursor: pointer;
      font-family: var(--sans);
      transition: all 0.15s;
    }
    .copy-btn:hover { background: rgba(255,255,255,0.14); color: #fff; }

    /* ── FOOTER ──────────────────────────────────────── */
    footer {
      background: var(--bg);
      border-top: 1px solid var(--border);
      text-align: center;
      padding: 28px 24px;
      font-size: 0.78rem;
      color: var(--text-muted);
    }

    footer a { color: var(--accent2); text-decoration: none; }
    footer a:hover { text-decoration: underline; }

    /* ── DIVIDER ─────────────────────────────────────── */
    .divider {
      height: 1px;
      background: var(--border);
      max-width: 860px;
      margin: 0 auto;
    }

    /* ── RESPONSIVE ──────────────────────────────────── */
    @media (max-width: 600px) {
      .abstract-card { padding: 22px 20px; }
      .links-row { gap: 8px; }
      section { padding: 48px 20px; }
      .dataset-section { padding: 48px 20px; }
      .bibtex-section { padding: 48px 20px; }
    }

    /* ── ANIMATIONS ──────────────────────────────────── */
    @keyframes fadeUp {
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<!-- ═══ HERO ══════════════════════════════════════════ -->
<header class="hero">
  <div class="venue-badge">Findings of ACL 2026</div>

  <h1>Multilingual Refusal Alignment<br>for Safer Large Language Models</h1>

  <div class="authors">
    <div class="name-list">
      <span>Aleksandra KrasnodΔ™bska<sup>†</sup></span>
      <span>Wojciech Kusa<sup>†</sup></span>
      <span>Aldo Lipani<sup>‑</sup></span>
    </div>
    <div class="affil">
      <sup>†</sup> NASK National Research Institute, Warsaw, Poland &nbsp;Β·&nbsp;
      <sup>‑</sup> University College London, London, UK<br>
    </div>
  </div>

  <div class="links-row">
    <a href="https://wojciechkusa.github.io/papers/RefusEU-2026.pdf" class="btn btn-primary">
      <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><polyline points="14 2 14 8 20 8"/></svg>
      Paper
    </a>
    <a href="https://huggingface.co/datasets/NASK-PIB/RefusEU" class="btn btn-dataset" target="_blank">
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      RefusEU Dataset
    </a>
    <a href="#bibtex" class="btn btn-outline">
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      BibTeX
    </a>
  </div>
</header>


<!-- ═══ ABSTRACT ══════════════════════════════════════ -->
<section class="abstract-section">
  <div class="section-label">Abstract</div>
  <div class="abstract-card">
    <p>As Large Language Models are deployed globally, ensuring consistent safety across languages becomes paramount. Yet safety behaviors vary unpredictably between languages, and most alignment research remains English-centric β€” creating a critical gap for non-English speakers worldwide.</p>
    <p>We systematically investigate the dynamics of multilingual alignment: whether single-language alignment transfers cross-lingually, how language consistency is preserved during training, and the resulting trade-offs with general capabilities. We introduce <strong>RefusEU</strong>, a novel refusal alignment dataset covering <strong>12 European languages</strong>, including a held-out test set for evaluating state-of-the-art models.</p>
    <p>Our controlled Direct Preference Optimization (DPO) experiments reveal two key insights: aligning models exclusively in English is <em>insufficient</em> to ensure cross-lingual safety β€” even for identical harm categories. Conversely, training on multilingual datasets can improve safety <em>without degrading general performance</em>, as measured by the Global MMLU benchmark.</p>
  </div>
</section>

<div class="divider"></div>

<!-- ═══ RESEARCH QUESTIONS ════════════════════════════ -->
<section>
  <div class="section-label">Research Questions</div>
  <h2>What We Set Out to Answer</h2>
  <ul class="rq-list">
    <li class="rq-item">
      <span class="rq-tag">RQ1</span>
      <p class="rq-text">Do we need to perform multilingual alignment for each language on the same groups of prompts, or is training in a single language (English) sufficient to achieve cross-lingual safety?</p>
    </li>
    <li class="rq-item">
      <span class="rq-tag">RQ2</span>
      <p class="rq-text">How well is cross-lingual consistency β€” the ability to respond in the prompt's language β€” preserved during multilingual training, and how does it interact with safety?</p>
    </li>
    <li class="rq-item">
      <span class="rq-tag">RQ3</span>
      <p class="rq-text">How does multilingual safety alignment influence general multilingual capabilities, including factual knowledge, fluency, and linguistic correctness?</p>
    </li>
  </ul>
</section>

<div class="divider"></div>

<!-- ═══ DATASET ═══════════════════════════════════════ -->
<div class="dataset-section">
  <div class="dataset-inner">
    <div class="section-label">Dataset</div>
    <h2>Introducing RefusEU</h2>
    <p>RefusEU is the first European dataset designed for alignment training as DPO-ready triples β€” (question, chosen, rejected) β€” and includes a separate, contamination-free test split. Each chosen response is a high-quality refusal; each rejected response was generated by a safety-abliterated model.</p>

    <div class="dataset-stats">
      <div class="stat-box">
        <span class="stat-number">12</span>
        <span class="stat-label">European languages</span>
      </div>
      <div class="stat-box">
        <span class="stat-number">4k+</span>
        <span class="stat-label">pairs per language</span>
      </div>
      <div class="stat-box">
        <span class="stat-number">14</span>
        <span class="stat-label">harm categories</span>
      </div>
      <div class="stat-box">
        <span class="stat-number">1,400</span>
        <span class="stat-label">test samples / lang.</span>
      </div>
    </div>

    <p style="margin-bottom:10px;"><strong style="color: var(--text-primary);">Languages covered:</strong></p>
    <div class="lang-pills">
      <span class="lang-pill">English</span>
      <span class="lang-pill">German</span>
      <span class="lang-pill">French</span>
      <span class="lang-pill">Italian</span>
      <span class="lang-pill">Spanish</span>
      <span class="lang-pill">Portuguese</span>
      <span class="lang-pill">Polish</span>
      <span class="lang-pill">Czech</span>
      <span class="lang-pill">Slovak</span>
      <span class="lang-pill">Slovenian</span>
      <span class="lang-pill">Lithuanian</span>
      <span class="lang-pill">Latvian</span>
    </div>

    <p style="margin-top: 20px;">Questions are generated using an adversarial pipeline based on Rainbow Teaming across 10 attack styles and 14 crime categories (Llama-Guard taxonomy). A multi-model labelling protocol (Llama-Guard-3-8B, PolyGuard-Qwen, GPT-4o-mini) ensures label quality, with a manual audit confirming 100% accuracy across 1,200 sampled pairs.</p>

    <div class="figure-block" id="fig-dataset">
      <img src="img/dataset.png" alt="Dataset construction pipeline" class="figure-image" style="width: 50%; margin: 0 auto;" />
      <div class="figure-caption">Figure 1 β€” Dataset construction process: adversarial prompt generation β†’ multilingual translation β†’ dual-model safety labelling β†’ DPO triple curation.</div>
    </div>
  </div>
</div>

<div class="divider"></div>

<!-- ═══ METHODOLOGY ═══════════════════════════════════ -->
<section>
  <div class="section-label">Methodology</div>
  <h2>Experimental Design</h2>
  <p>To isolate alignment dynamics, we start from <em>abliterated</em> Llama-3.1-8B and 70B models β€” versions where safety mechanisms have been deliberately removed via refusal direction ablation β€” then realign them using DPO under four dataset configurations:</p>

  <div class="highlights-grid" style="margin: 20px 0 24px;">
    <div class="highlight-card">
      <span class="highlight-icon">βš–οΈ</span>
      <div class="highlight-title">Balanced</div>
      <p>All 12 languages with equal representation (34,668 samples total).</p>
    </div>
    <div class="highlight-card">
      <span class="highlight-icon">🌍</span>
      <div class="highlight-title">High-Resource Only</div>
      <p>English, German, Italian, French, Spanish, Portuguese (17,334 samples).</p>
    </div>
    <div class="highlight-card">
      <span class="highlight-icon">πŸ‡¬πŸ‡§</span>
      <div class="highlight-title">English Only</div>
      <p>Baseline to test whether English alignment is sufficient (2,889 samples).</p>
    </div>
    <div class="highlight-card">
      <span class="highlight-icon">🌐</span>
      <div class="highlight-title">No English</div>
      <p>All 11 non-English languages β€” tests transfer to English from others.</p>
    </div>
  </div>

  <p>Additionally, 11 individual single-language DPO runs were performed to measure language-specific transfer. Evaluation uses Attack Success Rate (ASR) on RefusEU-test, language consistency, Global MMLU, and an LLM-as-a-Judge fluency/correctness protocol.</p>
</section>

<div class="divider"></div>

<!-- ═══ RESULTS ════════════════════════════════════════ -->
<section>
  <div class="section-label">Results</div>
  <h2>Key Findings</h2>

  <div class="figure-block" id="fig-asr">
    <img src="img/asr-table.png" alt="ASR comparison table results" style="width: 100%; display: block; margin: 0 auto 16px;" />
    <img src="img/asr.png" alt="ASR comparison figure results" style="width: 100%; display: block; margin: 0 auto;" />
    <div class="figure-caption">Table 2 and Figure 2 β€” Attack Success Rate (ASR %) on RefusEU-test. Lower is better. Balanced multilingual training achieves the lowest ASR across both model sizes.</div>
  </div>

  <ul class="finding-list">
    <li class="finding-item">
      <div class="finding-dot"></div>
      <p class="finding-text"><strong>English-only alignment is insufficient.</strong> Training exclusively on English safety preferences leads to notably higher ASR for low-resource languages, particularly with Llama-70B β€” demonstrating that cross-lingual safety transfer from English alone cannot be relied upon.</p>
    </li>
    <li class="finding-item">
      <div class="finding-dot"></div>
      <p class="finding-text"><strong>Balanced multilingual training works best.</strong> The lowest average ASR across all languages is consistently achieved by the balanced 12-language configuration for both the 8B and 70B models, with high-resource-only training as a strong second choice.</p>
    </li>
    <li class="finding-item">
      <div class="finding-dot"></div>
      <p class="finding-text"><strong>Linguistic proximity enables transfer.</strong> Closely related language pairs β€” Polish–Czech and Portuguese–Spanish β€” exhibit strongly correlated ASR values across training configurations, suggesting that structural similarity facilitates cross-lingual safety generalization.</p>
    </li>
    <li class="finding-item">
      <div class="finding-dot"></div>
      <p class="finding-text"><strong>Language consistency and safety interact non-trivially.</strong> While high language consistency is generally desirable, explicitly enforcing it can reduce safety in smaller models like Llama-8B. Llama-70B achieves near-100% consistency across all configurations; smaller models degrade under English-only setups.</p>
    </li>
    <li class="finding-item">
      <div class="finding-dot"></div>
      <p class="finding-text"><strong>General capabilities are largely preserved.</strong> Performance degradation on Global MMLU stays below 0.006 for both model sizes. For low-resource languages on the 8B model, translation-based pipelines (translate β†’ answer in English β†’ translate back) outperform native-language generation even for the unmodified Instruct baseline.</p>
    </li>
  </ul>

  <div class="figure-block" id="fig-scatter" style="margin-top: 24px;">
    <img src="img/language.png" alt="ASR vs. language consistency scatter plot" style="width: 90%; display: block; margin: 0 auto;" />
    <div class="figure-caption">Figure 3 β€” ASR vs. language consistency across training setups. Llama-70B with high-resource training achieves the best combined performance.</div>
  </div>
</section>

<div class="divider"></div>

<!-- ═══ CONTRIBUTIONS ═════════════════════════════════ -->
<section>
  <div class="section-label">Contributions</div>
  <h2>Summary</h2>
  <div class="highlights-grid">
    <div class="highlight-card">
      <span class="highlight-icon">πŸ—ƒοΈ</span>
      <div class="highlight-title">RefusEU Dataset</div>
      <p>The first DPO-ready multilingual refusal dataset covering 12 European languages, with a fixed contamination-free evaluation split and fully audited safety labels.</p>
    </div>
    <div class="highlight-card">
      <span class="highlight-icon">πŸ”¬</span>
      <div class="highlight-title">Controlled Experiments</div>
      <p>Systematic ablation across 4 training configurations + 11 single-language runs on deliberately de-safety-aligned base models for clean measurement.</p>
    </div>
    <div class="highlight-card">
      <span class="highlight-icon">πŸ“</span>
      <div class="highlight-title">Multidimensional Evaluation</div>
      <p>ASR, language consistency, Global MMLU, and fluency/correctness measured across all 12 languages, revealing trade-offs invisible under single-metric reporting.</p>
    </div>
  </div>
</section>

<!-- ═══ BIBTEX ════════════════════════════════════════ -->
<div class="bibtex-section" id="bibtex">
  <div class="bibtex-inner">
    <div class="section-label">Citation</div>
    <h2>BibTeX</h2>
    <div class="code-block" id="bibtex-block">
      <button class="copy-btn" onclick="copyBibtex()">Copy</button>
      <span class="code-brace">@inproceedings{</span><span class="code-val">krasnodebska2026refuseu</span><span class="code-brace">,</span><br>
      &nbsp;&nbsp;<span class="code-key">title</span>     = <span class="code-val">{Multilingual Refusal Alignment for Safer Large Language Models}</span>,<br>
      &nbsp;&nbsp;<span class="code-key">author</span>    = <span class="code-val">{KrasnodΔ™bska, Aleksandra and Kusa, Wojciech and Lipani, Aldo}</span>,<br>
      &nbsp;&nbsp;<span class="code-key">booktitle</span> = <span class="code-val">{Findings of the Association for Computational Linguistics: ACL 2026}</span>,<br>
      &nbsp;&nbsp;<span class="code-key">year</span>      = <span class="code-val">{2026}</span>,<br>
      &nbsp;&nbsp;<span class="code-key">address</span>   = <span class="code-val">{San Diego, California, United States}</span>,<br>
      &nbsp;&nbsp;<span class="code-key">publisher</span> = <span class="code-val">{Association for Computational Linguistics}</span><br>
      <span class="code-brace">}</span>
    </div>
  </div>
</div>

<footer>
  <p style="margin-top:6px;">RefusEU dataset available at <a href="https://huggingface.co/datasets/NASK-PIB/RefusEU" target="_blank">huggingface.co/datasets/NASK-PIB/RefusEU</a></p>
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