用密集检索替代生成模型，高效准确识别假新闻
When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection
- 用通用文本嵌入进行密集检索，搭配分类器完成事实验证
- 在RAWFC数据集上F1达65.58%，比顶尖生成模型高4.38个百分点
- 运行时间减少92%以上，适合大规模实时部署
虚假信息泛滥亟需高效可靠的事实核查系统。当前基于大语言模型（LLM）生成解释性推理的方法存在计算开销大、幻觉风险高等问题。本文提出轻量级框架DeReC（密集检索分类），利用通用文本嵌入替代自回归生成式LLM，在事实核查任务中实现更优性能。通过结合密集检索与专用分类器，DeReC在保持高准确率的同时显著提升效率：在RAWFC数据集上运行时间仅23分36秒（相比LLM的454分12秒降低95%），在LIAR-RAW上节省92%时间（134分14秒对1692分23秒）。在RAWFC上取得65.58%的F1分数，优于L-Defense的61.20%。结果表明，精心设计的检索系统可在特定任务中超越甚至匹配生成式模型表现，且更适合真实场景部署。
原文摘要 · Abstract (English)
The proliferation of misinformation necessitates robust yet computationally efficient fact verification systems. While current state-of-the-art approaches leverage Large Language Models (LLMs) for generating explanatory rationales, these methods face significant computational barriers and hallucination risks in real-world deployments. We present DeReC (Dense Retrieval Classification), a lightweight framework that demonstrates how general-purpose text embeddings can effectively replace autoregressive LLM-based approaches in fact verification tasks. By combining dense retrieval with specialized classification, our system achieves better accuracy while being significantly more efficient. DeReC outperforms explanation-generating LLMs in efficiency, reducing runtime by 95% on RAWFC (23 minutes 36 seconds compared to 454 minutes 12 seconds) and by 92% on LIAR-RAW (134 minutes 14 seconds compared to 1692 minutes 23 seconds), showcasing its effectiveness across varying dataset sizes. On the RAWFC dataset, DeReC achieves an F1 score of 65.58%, surpassing the state-of-the-art method L-Defense (61.20%). Our results demonstrate that carefully engineered retrieval-based systems can match or exceed LLM performance in specialized tasks while being significantly more practical for real-world deployment.
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