用科学数据微调大模型反而更易“信口开河”？最新多领域评估揭示幻觉风险，警示科学应用需谨慎。
arXiv:2606.21359v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to communicate and explain scientific concepts, yet…
针对MCP-based LLM智能体的来源感知事实性验证新方法，专治AI幻觉，提升输出可信度。
arXiv:2606.18037v1 Announce Type: new Abstract: Tool-using LLM agents increasingly use the Model Context Protocol (MCP) to answer from heterogeneous e…
将人类应试技巧引入模型判断声明事实性，有效提升大模型在证据支撑下的核查准确率。
arXiv:2605.29712v1 Announce Type: cross Abstract: Grounded claim factuality checking is important for large language model (LLM) applications such as …
研究揭示LLM在训练数据中植入错误信念后，即使明确警告也无法纠正，警示AI安全与事实性漏洞。
Fine-tuning tests show "bias ... toward confidently representing the claims as true."
精准揭示LLM幻觉根源，区分两种违规类型，为模型知识归因与纠错提供新思路。
arXiv:2602.22787v2 Announce Type: replace-cross Abstract: Large language model (LLM) hallucinations, meaning fluent but factually incorrect generation…
综述RAG系统可信度挑战，涵盖事实性、鲁棒性与公平性等关键维度。
arXiv:2409.10102v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the develo…