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

Xiaomeng Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26538 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ほとんどの評価は、食べる前に1枚の写真を使用し、エネルギーを摂取したと比較し、食事の評価はエネルギーを摂取する必要がある間隔を容易にします。私たちは3つの販売者からの8枚のモデル(Claude Haiku、Sonnet、Opus 5.5、GPT-6 luna、6.1 soldense、6 astra、Gemini 3.8 Flash、4 Argon)の802食材をACETADAから比較し、エネルギーを摂取するためのコントロールされた摂取試験で、エネルギーを摂取する前に3枚の販売者からの8枚のモデルを評価しました(サブトラクションを除く)、または1枚の要求で2枚の写真を含む3枚の写真を評価しました(サブトラクション、GPT-6 luna、6.1 soldense

Machine translation (Meta M2M100); the English original follows.

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 compare with energy served, while dietary assessment needs energy eaten. We evaluated eight models from three vendors (Claude Haiku, Sonnet and Opus 5.5; GPT-6 luna, 6.1 sol and 6 astra; Gemini 3.8 Flash and 4 Argon) on 802 meals from ACETADA, a controlled feeding trial with weighed intake, with the before photo only, before and after photos in separate calls (subtraction), or both photos in one request. Three results ran against intuition. Subtraction under-estimated intake for six of eight models, because the models still report hundreds of kilocalories for a nearly empty tray. Scaling the before-only estimate by the average share eaten (0.87) matched much of the gain from the after photo; beyond that constant, the second photo lowered meal-level error by only 2.2 to 4.4 points, mainly in meals with food left. With one photo, two models had a near-zero intake bias only because they under-estimated the served energy and ignored uneaten food. In ablations, adding the after photo improved accuracy under both prompt formats for seven models, while prompt effects varied across models. Over 148 feeding days, five models kept the 90% confidence interval of their mean error within a pre-set 10% margin; one model also met the pre-set 30% limits of agreement, narrowly (-28% to +27%; limits outside 30% cannot be excluded), and the other seven did not. Protein was under-estimated by every model, most in protein-dense meals; repeated calls changed error by at most 1.4 points; a high reasoning effort helped only the smallest model. A low average bias is not evidence of accurate individual intake estimation; such estimates should support, not replace, assessment by a dietitian.

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Topics: Calorie, Meal, Statistics, Energy (signal processing), Food intake, Food energy

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