Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures

Aizan Zafar, Kshitij Mishra, Asif Ekbal

TL;DR

A novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers that are logically sound, relevant, and engaging is proposed.

Abstract

In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. Through initial experimentation, we identified six pertinent first-order logical rules, which were then used to train a Logic-Understanding (LU) model capable of generating logical triples for a given context, question, and answer. These logic triples are then integrated into the training of MedLogic-AQA, enabling effective and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging. Evaluation with respect to both automated and human-based demonstrates the robustness of MedLogic-AQA against strong baselines. Through empirical assessments and case studies, we validate the efficacy of MedLogic-AQA in elevating the quality and comprehensiveness of answers in terms of reasoning as well as informativeness

MedLogic-AQA: Enhancing Medical Question Answering with Abstractive Models Focusing on Logical Structures

TL;DR

A novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers that are logically sound, relevant, and engaging is proposed.

Abstract

In Medical question-answering (QA) tasks, the need for effective systems is pivotal in delivering accurate responses to intricate medical queries. However, existing approaches often struggle to grasp the intricate logical structures and relationships inherent in medical contexts, thus limiting their capacity to furnish precise and nuanced answers. In this work, we address this gap by proposing a novel Abstractive QA system MedLogic-AQA that harnesses First Order Logic (FOL) based rules extracted from both context and questions to generate well-grounded answers. Through initial experimentation, we identified six pertinent first-order logical rules, which were then used to train a Logic-Understanding (LU) model capable of generating logical triples for a given context, question, and answer. These logic triples are then integrated into the training of MedLogic-AQA, enabling effective and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive QA equips our system to produce answers that are logically sound, relevant, and engaging. Evaluation with respect to both automated and human-based demonstrates the robustness of MedLogic-AQA against strong baselines. Through empirical assessments and case studies, we validate the efficacy of MedLogic-AQA in elevating the quality and comprehensiveness of answers in terms of reasoning as well as informativeness

Paper Structure

This paper contains 51 sections, 3 equations, 2 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Logic Understanding Module
  5. MedLogic-AQA
  6. Dataset
  7. Experiments
  8. Baselines
  9. Implementation Details
  10. Evaluation Metrics
  11. Automatic Evaluation:
  12. Human Evaluation:
  13. Results and Analysis
  14. Automatic Evaluation
  15. Human Evaluation
  16. ...and 36 more sections

Figures (2)

  • Figure 1: Illustration of Responses Generated by MedLogic-AQA: Demonstrating the approach's utilization of background knowledge and first-order logic-based rules to provide comprehensive answers to medical queries, exemplifying its logical reasoning capabilities"
  • Figure 2: Illustration of architecture of the MedLogic-AQA system. The Logic Understanding (LU) Module comprises several components: Context, Question, Logical Rule, and Answer. These components are input to the LLama2-7B model to generate logical knowledge triples. Subsequently, the LU module is fine-tuned using the context, logical rule, and question to generate the final answer.