Large language models (LLMs) are reshaping automated fact-checking (AFC) by enabling unified, end-to-end verification pipelines rather than isolated components. While large proprietary models achieve strong performance, their closed weights, complexity, and high costs limit sustainability. Fine-tuning smaller open weight models for individual AFC tasks can help but requires multiple specialized models resulting in high costs. We propose \textbf{multi-task learning (MTL)} as a more efficient alternative that fine-tunes a single model to perform claim detection, evidence ranking, and stance detection jointly. Using small decoder-only LLMs (e.g., Qwen3-4b), we explore three MTL strategies: classification heads, causal language modeling heads, and instruction-tuning, and evaluate them across model sizes, task orders, and standard non-LLM baselines. While multitask models do not universally surpass single-task baselines, they yield substantial improvements, achieving up to \textbf{44\%}, \textbf{54\%}, and \textbf{31\%} relative gains for claim detection, evidence re-ranking, and stance detection, respectively, over zero-/few-shot settings. Finally, we also provide practical, empirically grounded guidelines to help practitioners apply MTL with LLMs for automated fact-checking.

翻译：大型语言模型（LLM）正在重塑自动化事实核查（AFC），通过实现统一、端到端的验证流程，而非孤立的组件。虽然大型专有模型实现了强大的性能，但其封闭的权重、复杂性和高成本限制了可持续性。为单个AFC任务微调较小的开放权重模型可能有所帮助，但需要多个专用模型，导致成本高昂。我们提出**多任务学习（MTL）**作为一种更高效的替代方案，它通过微调单个模型来联合执行主张检测、证据排序和立场检测。使用小型仅解码器LLM（例如Qwen3-4b），我们探索了三种MTL策略：分类头、因果语言建模头和指令微调，并在模型大小、任务顺序和标准的非LLM基线模型上对它们进行了评估。虽然多任务模型并未普遍超越单任务基线，但它们带来了显著的改进，在零样本/少样本设置下，分别为主张检测、证据重排序和立场检测实现了高达**44%**、**54%**和**31%**的相对增益。最后，我们还提供了基于实证的实用指南，以帮助从业者将LLM与MTL应用于自动化事实核查。