A Dataset for Evaluating Large Language Models in Carbohydrate Estimation from Meal Descriptions
All data can be accessed in the data directory.
NutriBench consists of 5,000 human-verified meal descriptions with macro-nutrient labels, including carbohydrates, proteins, fats, and calories. It is divided into 15 subsets varying in meal description complexity.
The subsets are named according to the following format:
{serving_unit}_{retrieval_type}_{number_of_food}_{number_of_serving}.csv.
Where:
{serving_unit}: how the servings are measured in the description.{natural}(such as '1 cup') or{metric}(such as '50g').{retrieval_type}: to evaluate performance on RAG based methods we divided Nutribench in two. With the{direct}subsets, food items can be directly retrieved from a RAG DB with exact food name matches. With the{indirect}subsets there is no direct match between the queried food item name and the items in the RAG DB.{number_of_food}: the number of discrete food items in the description. One item{single}, two items{double}, or three items{triple}.{number_of_serving}: the number of servings in each description. One serving{single}, otherwise{multiple}.
For example:
natural_direct_single_multiple.csv indicates queries with natural serving unit, direct retrieval, a single food , and multiple servings of that single food.
# pip install pandas
import pandas as pd
file_path = 'data/metric_indirect_double_multiple.csv'
df = pd.read_csv(file_path)For any questions, please contact the authors at {dongx1997,mdhaliwal}@ucsb.edu or qinlab01@gmail.com