# A Conversational Agent System for Dietary Supplements Use

arXiv: https://arxiv.org/abs/2104.01543 · HTML: https://mlsanity.com/paper/2104.01543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [De-identification of Patient Notes with Recurrent Neural Networks](https://mlsanity.com/paper/1606.03475/) (2016)
- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [On Evaluating and Comparing Open Domain Dialog Systems](https://mlsanity.com/paper/1801.03625/) (2018)

## Built on by (most cited first, 2 of 2)

- [RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements](https://mlsanity.com/paper/2411.15700/) (2024)
- [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024)

## Abstract (arXiv)

Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively. Conversational agent (CA) systems have been applied to the healthcare domain, but there is no such a system to answer consumers regarding DS use, although widespread use of DS. In this study, we develop the first CA system for DS use