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English(EN) Benchmarking Large Language Models on Heterogeneous Cognitive and Biomedical Knowledge Graphs: The Role of Graph Grounding in Multi-Hop Reasoning and Hallucination Mitigation

LLM（如GPT-5.1和Gemini 2.0 Pro）在生物医学知识图谱推理方面展现出潜力

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文对五种大型语言模型（LLM）在处理异构认知和生物医学知识图谱方面的能力进行了基准测试。研究发现，像GPT-5.1和Gemini 2.0 Pro这样的通用模型在多跳推理方面通常优于领域特定模型，尽管随着查询复杂度的增加，性能有所下降。整合知识图谱显著降低了所有测试模型的幻觉率，强调了结合广泛的LLM能力与结构化领域知识的好处。 AI

影响 这项研究强调了将结构化领域知识与LLM相结合，以提高在生物医学等专业领域的准确性和减少幻觉的重要性。

排序理由 该集群包含一篇详细介绍LLM在特定知识图谱上进行基准测试的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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LLM（如GPT-5.1和Gemini 2.0 Pro）在生物医学知识图谱推理方面展现出潜力

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该集群包含一篇详细介绍LLM在特定知识图谱上进行基准测试的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ali Sarabadani, Kheirolah Rahsepar Fard, Tahereh Jeirani ·

    异构认知和生物医学知识图谱上的大语言模型基准测试：图谱接地在多跳推理和幻觉缓解中的作用

    arXiv:2611.08315v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in biomedical and cognitive neuroscience, but their ability to reason reliably over heterogeneous knowledge graphs (KGs) remains unclear. We benchmark five LLMs (GPT-5.1, Gemini 2.0…

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