Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

In recent years, deep-learning-based approaches for medical dialogue generation have become the predominant paradigm. However, real-world medical dialogues often face data imbalance issues, especially long-tail distribution problems. The scarcity of training samples for low-resource diseases makes it challenging for language models to provide accurate and comprehensive diagnostic support. In this paper, we propose MIRE, a novel framework that leverages external medical knowledge of tail diseases and dialogue data of common diseases to guide large language models (LLMs) in generating synthetic dialogues for tail diseases. Specifically, MIRE retrieves and crawls medical information about tail diseases from multiple online sources, enhancing subtype coverage in the generated synthetic dialogues. Moreover, we introduce a style transfer mechanism that utilises rich style templates extracted from common disease conversations to guide LLMs in augmenting dialogues in low-resource domains, thereby narrowing the gap between synthetic and real human dialogues. To evaluate the effectiveness of our method in addressing the long-tail disease problems, we construct a long-tail medical dialogue dataset, named TailMed. Experimental results show that training the model with a mixture of synthetic dialogues and the original dataset significantly improves both automatic metrics and human evaluations. Specifically, the model trained on the MIRE-enhanced dataset outperforms the original by over 20% in average metrics for tail diseases. These results demonstrate the potential of MIRE to enhance clinical dialogue systems, enabling more equitable diagnostic assistance for rare and underrepresented diseases, and contributing to improved accessibility in intelligent healthcare applications.

The rapid growth of the internet has led to an alarming increase in the dissemination of fake news, which has had many negative effects on society. Various methods have been proposed for detecting fake news. However, these approaches suffer from several limitations. First, most existing works only consider news as separate entities and do not consider the correlations between fake news and real news. Moreover, these works are usually conducted in the Euclidean space, which is unable to capture complex relationships between news, in particular the hierarchical relationships. To tackle these issues, we introduce a novelMulti-modalHyperbolicRepresentation framework (MHR) for fake news detection. Specifically, we capture the correlations between news for graph construction to arrange and analyze different news. To fully utilize the multi-modal characteristics, we first extract the textual and visual information, and then design a Lorentzian multi-modal fusion module to fuse them as the node information in the graph. By utilizing the fully hyperbolic graph neural networks, we learn the graph’s representation in hyperbolic space, followed by a detector for detecting fake news. The experimental results on three real-world datasets demonstrate that our proposed MHR model achieves state-of-the-art performance, indicating the benefits of hyperbolic representation.

In this paper, we extend financial sentiment analysis (FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and fine-grained event categories. Under this setting, we formulate the Event-Level Financial Sentiment Analysis(EFSA for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing 12,160 news articles and 13,725 quintuples is publicized as a brand new testbed for our task. A four-hop Chain-of-Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are available at https://github.com/cty1934/EFSA

In-context learning (ICL) has demonstrated excellent performance across various downstream NLP tasks, especially when synergized with powerful large language models (LLMs).Existing studies evaluate ICL methods primarily based on downstream task performance.This evaluation protocol overlooks the significant cost associated with the demonstration configuration process, i.e., tuning the demonstration as the ICL prompt.However, in this work, we point out that the evaluation protocol leads to unfair comparisons and potentially biased evaluation, because we surprisingly find the correlation between the configuration costs and task performance.Then we call for a twodimensional evaluation paradigm that considers both of these aspects, facilitating a fairer comparison.Finally, based on our empirical finding that the optimized demonstration on one language model generalizes across language models of different sizes, we introduce a simple yet efficient strategy that can be applied to any ICL method as a plugin, yielding a better trade-off between the two dimensions according to the proposed evaluation paradigm.