图拓扑感知+软提示，攻克不完整知识图谱推理难题，学术干货满满。
arXiv:2604.12503v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but rem…
LLM基准测试常因格式要求误判知识，Soft-Prompt Tuning让base模型公平展现真实能力。
arXiv:2606.12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e…
利用软提示蒸馏技术，在设备端实现安全大模型系统，兼顾效率与部署安全性。
arXiv:2606.09388v1 Announce Type: new Abstract: Deploying safe large language models (LLMs) on resource-constrained edge devices presents a critical c…
LLM与GNN巧妙联姻，用软提示融合关系图信息，为欺诈检测提供新范式。
arXiv:2605.28524v1 Announce Type: new Abstract: In recent years, Large Language Models (LLMs) have shown great capability in processing graph tasks su…
提出将LLM软提示自动翻译为人类可读硬提示的创新方法，提升提示可解释性与迁移性。
arXiv:2605.27642v1 Announce Type: cross Abstract: Soft prompt tuning is a parameter-efficient method for adapting LLMs to specific tasks, but suffers …
双LLM软提示新框架实现高效文本嵌入，迁移性更强、计算成本更低，值得NLP研究者关注
arXiv:2605.28066v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current ad…