Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models

Authors: Zhongzhen Huang, KUI XUE, Yongqi Fan, Linjie Mu, Ruoyu Liu, Tong Ruan, Shaoting Zhang, Xiaofan Zhang
Primary area: foundation or frontier models, including LLMs
Keywords: large language models, medication consultation, retrieval-augmented generation, tool call

Abstract:
Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. To mitigate these shortcomings, Retrieval-augmented generation (RAG) has been utilized to provide external knowledge to facilitate the answer generation. However, applying such models to the medical domain faces several challenges due to the lack of domain-specific knowledge and the intricacy of real-world scenarios.
In this study, we explore LLMs with RAG framework for knowledge-intensive tasks in the medical field. To evaluate the capabilities of LLMs, we introduce MedicineQA, a multi-round dialogue benchmark that simulates the real-world medication consultation scenario and requires LLMs to answer with retrieved evidence from the medicine database. MedicineQA contains 300 multi-round question-answering pairs, each embedded within a detailed dialogue history, highlighting the challenge posed by this knowledge-intensive task to current LLMs. We further propose a new \textit{Distill-Retrieve-Read} framework instead of the previous \textit{Retrieve-then-Read}. Specifically, the distillation and retrieval process utilizes a tool calling mechanism to formulate search queries that emulate the keyword-based inquiries used by search engines. With experimental results, we show that our framework brings notable performance improvements and surpasses the previous counterparts in the evidence retrieval process in terms of evidence retrieval accuracy. This advancement underscores the framework's potential to effectively address the inherent challenges of applying RAG models to the medical domain.

Decision: withdrawn
Reviewer scores: 6, 5, 6

Reviews and discussion:
<thread>
creation date:
1730433658956

thread_type:
official_review

<thread_object>
creator: reviewer_5hq1

<content>
summary:
The paper has introduced a benchmark called 'MedicineQA' to evaluate llms in medical consultation. Their suggested benchmark is focusing on rag for multi-turn dialogues in medication consultation. The authors also propose RagPULSE framework to deal with limitations of standard RAG frameworks. This framework utilizes a tool-calling mechanism to enhance retrieval accuracy by summarizing dialogue history into search queries and retrieving evidence from a medicine database.

strengths:
This paper shows an approach to enhancing LLMs in the medical domain by reformulating rag for multi-round medication consultations. Also, using a tool-calling mechanism for search query formulation is a novel method of tailoring retrieval processes in dialogue-based tasks in healthcare. The paper shows good quality in methodology. Also, the authors did around 600 experiments to validate the effectiveness of ragpulse and provided comparisons with existing open-source and commercial models. The paper has clear objectives, methods, and findings sections written. The study addresses a research gap in applying LLMs to the medical field and introduced MedicineQA, ragpulse. Their work shows good retrieval and response generation capabilities.

weaknesses:
I feel that the number of multi-round dialogues (300) are very less and that makes it difficult in terms of generalizing the results. So, maybe increase the dataset size by expanding the set of topics like for various specific conditions like breast cancer, diabetes, chronic pain.

Another thing is that the study should discuss retrieval errors for the cases where the model fails to retrieve accurate responses. The authors can try to include 'error analysis' section (maybe) that show various retrieval failures types or examples. This analysis will certainly go a long way in understanding if any adjustments should be made in distillation process.

questions:
1. How ragpulse deals with retrieval errors (if any)? Also, did you see any failure modes commonly found and can you discuss them, please?

2. The benchmark is available in English and Chinese as per the paper. but how well do we know if MedicineQA can be adapted to other languages (maybe low-resource languages)?
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</thread>
<thread>
creation date:
1730781398499

thread_type:
official_review

<thread_object>
creator: reviewer_gvnx

<content>
summary:
This paper introduces a benchmark, database and method for describing medications in dialogues with LLMs. The benchmark is collected automatically and reviewed by medical experts and the database is curated to support retrieval of medication information. The paper evaluates the proposed approach of converting conversations with their histories into a set of keywords to query the medical database and the results indicate favourable outcomes, based on GPT-4- and human-based evaluations of the generations.

strengths:
The collected resources seem helpful and, even if collected automatically, are validated by domain experts. The proposed approach seems to improve over LLM-based baselines.

The experiments seem sound in what they report/investigate.

The overall writing quality is good.

weaknesses:
My main concerns is the lack of meaningful comparison and under-specification of some of the details.

- While the paper suggests that the proposed distill-then-retrieve framework is better than RAG methods, I did not find meaningful comparisons with either general RAG frameworks as e.g. discussed in lines 117-126, or with domain-specific RAG approaches, such as e.g. BioReader (https://aclanthology.org/2022.emnlp-main.390.pdf) RAFT (https://arxiv.org/abs/2403.10131).

- The experimental setting is somewhat under-specified, see my questions to the authors. In summary, I don't understand the exact settings for the comparisons in Tables 2 and 3.

- There is very little detail on the construction of the medical database, which seems to be the key contribution enabling the retrieve-then-distill framework. Particularly, data sources, data quality measures etc are missing.

I gravitate towards rejection but if my queries are answered with sufficient detail, I am open to re-evaluating my decision.

questions:
1) What is the use of the question annotations in MedicineQA? If I understand correctly, it is the explicit reformulation of the questions from the conversation history (which might be ambiguous and contain superfluous information, as shown in "Dialogue History" in Figure 3). I understand that the synthetic dataset collected and described in lines 257-264 is used to convert questions into keywords, but during inference, what is actually used as input? Is it the dialogue history, of the extracted questions. If the latter, how are they extracted from dialogue history?

2) Could you please indicate how you measure the H@K for "baseline models". From reading lines 300-306, It is not clear to me what the baselines are actually doing. Are they generating the medication names from their parameters? Are they generating queries that are run against the database? 

3) Furthermore, could you please elaborate on what you mean by "we immediately adopt the attached corresponding medicine information as the context to guide the generation [...]" in line 323. Is it the ground-truth medicine annotations from MedicineQA? If so, I find this result highly counter-intuitive and contradictive, as the paper states later, in lines 389 ff, that the improvements of RagPULSE are due to the proposed RAG framework rather than due to domain fine-tuning of the PULSE LLM. But since RagPULSE's retrieval is far from optimal (e.g. H@k rates in Tables 2 and 3), it would suggest that imperfect retrieval somehow leads to better generations. Similarly, could you please indicate what "instruction used for baseline models" in the description of Table 3 means: "We also prompt RagPULSE by the instruction used for baseline models, which are denoted as †".
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</thread>
<thread>
creation date:
1730786035102

thread_type:
official_review

<thread_object>
creator: reviewer_rjmn

<content>
summary:
This paper built a multiple-round dialogue benchmark MedicineQA and proposed a distill-retrieve-read framework for RAG improved medication consultation capability of LLM. The dataset size is limited, and the multiple-round dialogues were generated by GPT-4 but not from real-world dialogue; this may affect the value of the application for the proposed framework. More details about the dataset should be introduced, such as the number of dialogue rounds.  It seems the dialogue distilling tool was trained on the general domain synthetic dataset but not for the medical domain, and the details of the dialogue distill tool training are not clear (which base model? data size? training settings? list of data sources). The proposed method lacks novelty; it applies the dialogue distilling module in the RAG setting and tests on the small MedicineQA dataset. Experiments are only based on a single dataset.
Typo? The manuscript mentioned "Table 3.2," but this is not Table 3.2; I guess it should be Table 1.
Question: Will the medicine database be released to the public?

strengths:
This paper proposed a new dataset for medication consultation.

weaknesses:
The dataset size is limited, and the multiple-round dialogues were generated by GPT-4 but not from real-world dialogue; this may affect the value of the application for the proposed framework. More details about the dataset should be introduced, such as the number of dialogue rounds.  It seems the dialogue distilling tool was trained on the general domain synthetic dataset but not for the medical domain, and the details of the dialogue distill tool training are not clear (which base model? data size? training settings? list of data sources). The proposed method lacks novelty; it applies the dialogue distilling module in the RAG setting and tests on the small MedicineQA dataset.  Experiments are only based on a single dataset.

questions:
Typo? The manuscript mentioned "Table 3.2," but this is not Table 3.2; I guess it should be Table 1.
Question: Will the medicine database be released to the public?
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</thread>
<thread>
creation date:
1731934992140

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Motivation of Our Paper

comment:
We sincerely appreciate all reviewers’ time and effort in reviewing our paper. We are pleased that the reviewers generally appreciate MedicineQA. We are willing to release a subset of the medicine database to support research and applications. Currently, we are in the process of translating and validating subsets of the database. We regret any delays in making this resource available as an open-source tool and appreciate your understanding.

# Motivation

We hope the following clarification will provide a more comprehensive understanding of our work. In recent years, large language models (LLMs) have demonstrated impressive capabilities and can be repurposed with minimal effort across numerous domains and diverse tasks. These models have shown great promise in medicine, with applications including primary care, clinical decision support, and medication consultation. However, the bar for clinical applications is particularly high due to the safety-critical nature of the medical domain. This is especially important for LLMs, as they are prone to hallucinating convincing but potentially harmful medical misinformation. Previous works sought to incorporate relevant evidence via Retrieval Augmented Generation. The paradigm of retrieving external knowledge holds great promise for knowledge-intensive tasks like medication consultation. Nevertheless, attempts to assess the capabilities of RAG-based LLMs in medical scenarios are limited. To address this gap, we introduce MedicineQA, a benchmark comprising 300 high-quality, expert-annotated multi-round dialogues spanning ten key aspects of medication consultation that users commonly encounter on online consultation platforms. 

MedicineQA focuses on commonly used medicines to meet a wide range of prevalent needs. Our goal is for models that perform well on MedicineQA to provide basic healthcare support in regions with relatively scarce medical resources. Besides, we harness the trivial RAG-based LLMs with a novel *Distill-Retrieve-Read* framework to access the external knowledge. The model processes the inquiry by summarizing the dialogue history into keywords, mimicking the process of calling search engines, and then integrating the retrieved evidence to formulate a comprehensive response.

# Revised Paper

According to the reviewers' comments, we have revised our paper and marked the modified parts in blue for clarity.
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</thread>
<thread>
creation date:
1732862128468

thread_type:
official_comment

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creator: authors

<content>
comment:
Dear Reviewers,

We would like to extend our heartfelt gratitude for your valuable and constructive feedback during the previous round of discussions. Your insights have been instrumental in refining and improving our revised manuscript.

In this paper, we present MedicineQA, a dataset centered on commonly used medicines, designed to address a wide range of prevalent healthcare needs. Our goal is for models excelling on MedicineQA to provide basic healthcare support in regions with limited medical resources. Additionally, we are committed to offering proofreading and translation services for a subset of our medicine database to ensure comprehensive and accessible support. We hope our work will inspire further innovation in applying large language models (LLMs) to the medical domain and help bridge the gap between these models and real-world applications.

Given the extended timeline, we humbly and sincerely request the opportunity to continue our dialogue with you to fully meet your expectations. If possible, we kindly ask you to reassess our revised manuscript. If the updates adequately address your concerns, we would be deeply grateful if you would consider revisiting and potentially revising your score. If there are still unresolved issues or specific areas where the manuscript could be further improved, we would greatly appreciate any additional feedback. Your input remains invaluable in helping us achieve the highest standard for this work.

Thank you once again for your time, thoughtful feedback, and continued engagement.

Best regards,

The Authors
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