Fengyuan Liu, Yi Huang, Sichun Luo, Yuqi Wang, Yazheng Yang, Xinye Li, Zefa Hu, Junlan Feng, Qi Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

An increasing number of individuals are turning to online self-diagnosis by matching their symptoms with potential medical conditions. This process involves two primary components: symptom inquiry and disease prediction. Existing works employ two separate modules to learn these tasks individually. Nevertheless, this intuitive approach encounters low data efficiency due to the separate learning of each module. In addition, previous research incorporates symptom statuses solely as part of the input without any additional modeling. However, this oversight neglects the importance of symptom status, which indicates whether the user has experienced the symptom. The status significantly influences both symptom inquiry strategies and disease prediction. To address these challenges, we propose a Status-Aware Mask Prediction Framework for online disease diagnosis, called SA-MPF. SA-MPF formalizes symptom inquiry and disease prediction as a single masked token prediction task, distinguishing them solely through the masked token type. Furthermore, we introduce a masked status prediction task, which unifies the prediction of symptom or disease statuses in a similar manner to masked token prediction, thereby enhancing the modeling of symptom and disease statuses. We evaluate SA-MPF on several datasets collected from various sources. The experimental results demonstrate substantial improvements achieved by SA-MPF. For example, on the GMD-12 dataset, SAMPF demonstrates a noteworthy 5% improvement in diagnostic accuracy, from 82% to 87%.1

Large language models (LLMs) excel at providing general and comprehensive health advice in single-turn dialogues. However, the limited information in single-turn conversations provided by users results in generated advice lacking personalization and specificity. In real-world medical consultations, doctors typically gain a comprehensive understanding of a patient’s condition through a series of iterative inquiries, enabling them to subsequently offer effective and personalized advice. To enhance capabilities similar to those of doctors, existing approaches often learn by increasing multi-turn medical dialogue corpora. In this study, we consider capturing the transitions of medical terms in each turn crucial, as they aid in understanding the flow of the conversation and enhance the accuracy of generating medical term information in the next turn. Therefore, we propose a Term-aware Agent (T-Agent) and develop a corresponding term extraction tool and term prediction model. T-Agent explicitly models the flow of term information in the dialogue by invoking the term extraction tool and the term prediction model. To better learn the term prediction task, we adopt a two-stage training approach. In the first stage, we conduct mixed training on a single large model, simultaneously learning term prediction and the ability of T-Agent to invoke term tools for dialogue. This mixed training in the first stage allows the large model to initially adapt to the term prediction task. In the second stage, we independently train the term prediction model and T-Agent on this basis, enhancing their expertise and performance in their respective tasks. We validated the effectiveness of the proposed method on two Chinese multi-turn medical dialogue datasets, demonstrating significant performance improvements, particularly in the accuracy of term information within dialogues.

Generative retrieval has garnered significant attention for its end-to-end optimization and exceptional performance. Compared with the dense retrieval paradigm, the generative retrieval paradigm maps a query to a relevant document ID only relying on its model parameters, greatly simplifying the retrieval process. However, generative retrieval faces two challenges: it does not explicitly model the semantic relevance between query and document, and there exists a gap between the representation of query and document. To this end, we propose the Contrastive Search Index (ConSI), a simple but effective contrastive learning framework for generative document retrieval, to address the above challenges. Experiments show that the proposed ConSI consistently surpasses the previous generative retrieval baselines, NCI. Further analysis of different factors and indicators verifies the performance enhancement brought by our method. Besides, our ConSI also achieves excellent performance in the dense retrieval paradigm, demonstrating that the designed framework boosts representation learning ability and can be directly used as a dense retriever.

Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems. However, the lack of sufficient term semantics learning makes existing approaches hard to capture semantically identical but colloquial expressions of terms in medical conversations. In this work, we formalize MSF into a matching problem and propose a Term Semantics Pre-trained Matching Network (TSPMN) that takes both terms and queries as input to model their semantic inter-action. To learn term semantics better, we further design two self-supervised objectives, including Contrastive Term Discrimination (CTD) and Matching-based Mask Term Modeling (MMTM). CTD determines whether it is the masked term in the dialogue for each given term, while MMTM directly predicts the masked ones. Experimental results on two Chinese benchmarks show that TSPMN outperforms strong baselines, especially in few-shot settings1.