NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
Diet plays a critical role in human health, yet tailoring dietary reasoning
to individual health conditions remains a major challenge. Nutrition Question
Answering (QA) has emerged as a popular method for addressing this problem.
However, current research faces two critical limitations. On one hand, the
absence of datasets involving user-specific medical information severely limits
\textit{personalization}. This challenge is further compounded by the wide
variability in individual health needs. On the other hand, while large language
models (LLMs), a popular solution for this task, demonstrate strong reasoning
abilities, they struggle with the domain-specific complexities of personalized
healthy dietary reasoning, and existing benchmarks fail to capture these
challenges. To address these gaps, we introduce the Nutritional Graph Question
Answering (NGQA) benchmark, the first graph question answering dataset designed
for personalized nutritional health reasoning. NGQA leverages data from the
National Health and Nutrition Examination Survey (NHANES) and the Food and
Nutrient Database for Dietary Studies (FNDDS) to evaluate whether a food is
healthy for a specific user, supported by explanations of the key contributing
nutrients. The benchmark incorporates three question complexity settings and
evaluates reasoning across three downstream tasks. Extensive experiments with
LLM backbones and baseline models demonstrate that the NGQA benchmark
effectively challenges existing models. In sum, NGQA addresses a critical
real-world problem while advancing GraphQA research with a novel
domain-specific benchmark.
