KG-enhanced LLMs KG-enhanced LLM Pre-training KG-enhanced LLM Inference KG-enhanced LLM Interpretability LLM-augmented KGs LLM-augmented KG Embedding LLM-augmented KG Completion LLM-augmented KG-to-Text Generation LLM-augmented KG Question Answering ...
To summarize, our contributions are: (1) We propose KG-Rank, a KG-enhanced LLM framework for medical QA tasks. To the best of our knowledge, this is the first application of KG and ranking-enhanced LLMs to medical QA with long answers. (2) We incorporate ranking techniques to improve f...
which have enhanced LLMs’ performance and reliability. These techniques have demonstrated that LLMs ...
将从多个图像中局部提取的三元组与大规模KG对齐可以看作是上述二者的混合。这种混合方法的优势是双重的:它扩大了图像数量的覆盖范围(第一种范式),还融入了第二种范式特有的广泛知识规模,这可以促进大规模、三元组级别的多模态信息生成,为未来在多模态实体对齐和MMKG驱动的应用(如MLLM预训练和VQA)提供新的机遇。 (...
(IFT), models processed by KG-LLM framework have greatly enhanced the accuracy of predictions in KG-related tasks. Overall, the standard framework models have displayed relatively lower performance in multi-hop link prediction tasks, and the introduction of in-context learning (ICL) has further ...
KG-LLM-Papers What can LLMs do for KGs? Or, in other words, what role can KG play in the era of LLMs? 🙌 This repository collects papers integratingknowledge graphs (KGs)andlarge language models (LLMs). 😎 Welcome to recommend missing papers throughAdding IssuesorPull Requests. ...
knowledge-enhanced language understanding and generation. 2- Historical Perspectives: Insights into the evolution of KGs and LLMs, tracing their development trajectories, seminal works, and transformative milestones leading to their integration. 3- Design and Implementation: Research articles focusing on the...
成. 15.6 bert 的改进版有哪些 参考答案:RoBERTa:更强大的 BERT 加大训练数据 16GB -> 160GB,更大的batch size,训练时间加长 不需要 NSP Loss:natural inference 使用更长的训练 SequenceStatic vs. Dynamic Masking 模型训练成本在 6 万美金以上(估算) ALBERT:参数更少的 BERT一个轻量级的 BERT 模型 共享层与...
将从多个图像中局部提取的三元组与大规模KG对齐可以看作是上述二者的混合。这种混合方法的优势是双重的:它扩大了图像数量的覆盖范围(第一种范式),还融入了第二种范式特有的广泛知识规模,这可以促进大规模、三元组级别的多模态信息生成,为未来在多模态实体对齐和MMKG驱动的应用(如MLLM预训练和VQA)提供新的机遇。
对了，这个推理也是基于LLM的。通过few shot来实现的。对于这个推理，结合对应的剪枝后的子图，我们利用...