M ed L ogic- AQA : Enhancing Medicare Question Answering with Abstractive Models Focusing on Logical Structures (2024)

Abstract: In Medicare 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-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 MediLogic-AQA, enabling reasoned and coherent reasoning during answer generation. This distinctive fusion of logical reasoning with abstractive question answering 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.

## introduction in recent years, the demand for effective questionanswering (qa) systems in the field of medicine has surged, driven by the need to provide accurate and informative responses to complex medical inquiries @xcite . with the proliferation of medical data and the increasing reliance on digital platforms for healthcare information, the development of robust qa systems has become imperative to support medical professionals and patients alike @xcite . existing abstractive question-answering (aqa) approaches in medicine face significant challenges in capturing the intricate logical structures and relationships inherent in medical contexts @xcite @xcite . this leads to sub-optimal outcomes @xcite , i.e. limiting the aqa systems to furnish precise and nuanced answers to medical queries necessitating logical reasoning @xcite . through the integration of logical reasoning in aqa systems, they can navigate the complexities of medical data and provide reasoned, coherent, and informative answers to medical queries @xcite . therefore, to address the limitations of the existing approaches, we propose a novel abstractive qa system, medlogic-aqa. central to our approach is the conceptualization of the logical structure of the context as a graph. we achieve this by employing six carefully chosen first order logical (fol) rules, with nodes representing entities and edges encapsulating their logical relationships. this structured representation facilitates a more nuanced understanding of the context, enabling us to navigate intricate logical dependencies effectively. utilizing this graph-based information as output and with the context, question, and answer as input, we initially train a logic-understanding (lu) model using llama2 @xcite . subsequently, we fine-tune the lu model with the input of context, question, and the desired output answer. this fine-tuning process emphasizes logical coherence and contextual relevance, enhancing the generation of answers. this logic-based representation offers a flexible and scalable framework for integrating logical rules, making it applicable across diverse domains and datasets. an example of medlogic-aqa is shown in 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 1 . it can be seen that medlogic-aqa first establishes the background knowledge of different entities involved with the user to provide the answer for a better understanding. to perform reasoning, medlogic-aqa leverages first-order logic-based rules extracted from both context and questions to generate well-grounded answers that encapsulate the underlying logical reasoning behind medical concepts and relationships @xcite . our key contributions can be summarized as follows: ## related work abstractive question answering (aqa) has witnessed substantial research efforts, with several approaches aiming to enhance the generation of contextually relevant and coherent answers @xcite @xcite . the pursuit of effective question-answering (qa) systems in medical domain has garnered considerable attention in the recent years @xcite . this surge in interest stems from the critical necessity of furnishing accurate and informative responses to intricate medical inquiries amidst the proliferation of medical data and the increasing reliance on digital platforms for healthcare information @xcite . existing literature primarily falls into two categories: methods leveraging neural networks and those incorporating logical reasoning. neural network-based approaches: early endeavors in aqa predominantly focused on neural network-based models, often employing recurrent neural networks (rnns) and later transitioning to attention mechanisms and transformers @xcite . notable works include the introduction of sequence-to-sequence models @xcite . while these methods demonstrated promising results, they struggled to capture intricate logical structures and dependencies within the context, limiting their ability to handle complex queries that require nuanced reasoning. logical reasoning in question answering: recognizing the limitations of neural networkcentric approaches, researchers delved into incor-porating logical reasoning to imbue aqa systems with enhanced inferential capabilities @xcite @xcite . early attempts utilized knowledge graphs and semantic parsing to introduce explicit logical structures @xcite . however, these methods faced challenges in scalability and were often domain-specific. graph-based representations: recent advancements in graph-based representations @xcite have offered a more versatile and scalable approach to capturing logical relationships within text. graph neural networks (gnns) have shown promise in modeling dependencies and hierarchies in various natural language processing tasks @xcite @xcite . however, the application of gnns in aqa @xcite has been limited, and their efficacy in handling logical rules derived from the context remains an under-explored area. in healthcare, several attempts have been made to develop persuasive @xcite and counseling conversation systems @xcite @xcite . however, these systems primarily focus on enhancing meta-communicative aspects, such as politeness, empathy, and personalization, rather than generating context-sensitive responses. specifically, within the domain of medical care, while there has been work in the field of medicine, current qa approaches face significant challenges in capturing the complex logical structures and relationships inherent in medical contexts @xcite @xcite . the inability to effectively discern intricate logical patterns within medical data often leads to sub-optimal results, impacting both the accuracy and relevance of the answers provided @xcite . these limitations hinder the ability of qa systems to offer precise and nuanced responses to medical queries that demand logical reasoning @xcite . @xcite proposed a logic-based approach that leverages first-order logic rules extracted from both the context and questions to generate wellgrounded answers, incorporating the underlying logical reasoning embedded within medical concepts and relationships. this work bridges the gap between neural network-based aqa models and logical reasoning by proposing a novel framework that leverages first-order logic-based rules extracted from the context, represented as a graph. our approach draws inspiration from minsky's seminal work on knowledge representation @xcite , aiming to integrate explicit logical structures into the aqa process. additionally, the attention mechanism proposed by @xcite serves as a cornerstone in our approach, facilitating the nuanced integration of logical rules into the abstractive questionanswering paradigm. unlike previous works, our method focuses on the extraction of logical rules directly from the context, enabling a more dynamic and context-aware system. ## methodology the proposed system medlogic-aqa involves two components, viz. (i.) logic understanding module -responsible for infusing logical rules into the model's decision-making process. it plays a critical role in enhancing the model's reasoning capabilities, making it adept at understanding complex relationships and dependencies within the data. (ii.) medaqa module -this step utilizes lu's logical reasoning capabilities to refine the model's understanding of complex dependencies to generate logically correct and contextually relevant answers as per first-order logic rules. the two-stage fine-tuning approach is detailed in section e of the appendix. ## dataset our experiments are conducted on two benchmark datasets: mash-qa @xcite and the bioasq task 10b phase b (qa task) dataset @xcite . the bioasq task 10b phase b @xcite dataset is meticulously designed for biomedical qa, encompassing tasks like biomedical semantic indexing and qa, with a specific emphasis on the qa task. on the other hand, the mash-qa @xcite dataset consists of consumer healthcare questions extracted from webmd, covering diverse healthcare sectors and addressing common healthcare concerns. with approximately 25k question-answer pairs, it is the largest dataset available in the medical domain. for detailed dataset statistics and pre-processing information, please refer to the appendix a. ## results and analysis in assessing the medlogic-aqa performance, we employ both quantitative (tables 1 and 2 ) and qualitative analyses ( ## conclusion our work presents medlogic-aqa, an innovative aqa system that addresses the inherent challenges of capturing complex logical structures within contextual information. by leveraging firstorder logic-based rules and adopting a graph-based representation, our system demonstrates a significant advancement in enhancing reasoning capabilities for nuanced and intricate queries. it excels in generating contextually rich answers, surpassing the limitations of existing methods, hence, facilitating a more structured understanding of information by incorporating logical rules derived from the context. this ensures that the generated answers not only align with the given query but also adhere to logical constraints within the text. as we move forward, continued research and refinement of medlogic-aqa hold the potential to further elevate its performance and broaden its applicability across diverse domains, establishing a foundation for the next generation of advanced abstractive question answering system.