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English(EN) Small Average Error, Wide Individual Error: General-Purpose LLMs Estimating Energy Intake from Meal Photos in a Feeding Trial

研究发现：大语言模型在个体餐食能量估算方面存在困难

作者 PulseAugur 编辑部 · [1 个来源] ·

一项新研究评估了八个通用大语言模型在受控喂养试验中从餐食照片估算能量摄入的能力。研究发现，虽然一些模型显示出较低的平均误差，但其个体估算值差异很大。值得注意的是，使用餐前和餐后照片对大多数模型来说，其准确性并未显著优于仅使用餐前照片，并且蛋白质摄入量一直被低估。研究结果表明，尽管大语言模型功能强大，但它们尚未达到足以取代人类营养师进行精确饮食评估的可靠性。 AI

影响 大语言模型在饮食评估方面显示出潜力，但缺乏取代人类专家所需的个体准确性。

排序理由 该集群包含一篇详细介绍大语言模型能力研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

研究发现：大语言模型在个体餐食能量估算方面存在困难

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该集群包含一篇详细介绍大语言模型能力研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaomeng Li ·

    Small Average Error, Wide Individual Error: General-Purpose LLMs Estimating Energy Intake from Meal Photos in a Feeding Trial

    arXiv:2610.26538v2 Announce Type: replace-cross Abstract: Photo-based estimation of food energy could ease dietary assessment, and general-purpose multimodal large language models (LLMs) make such estimates easy to obtain. Most evaluations use one photo taken before eating and co…

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