7 accepted papers
Biomedical Terminology Normalization aims to identify the standard term in a specified termbase for non-standardized mentions from social media or clinical texts, employing the mainstream “Recall and Re-rank” framework. Instead of the traditional pretraining-finetuning paradigm, we would like to exp…
This study focuses on evaluating proactive communication and diagnostic capabilities of medical Large Language Models (LLMs), which directly impact their effectiveness in patient consultations. In typical medical scenarios, doctors often ask a set of questions to gain a comprehensive understanding o…
Integrating tools into Large Language Models (LLMs) has facilitated the widespread application. Despite this, in specialized downstream task contexts, reliance solely on tools is insufficient to fully address the complexities of the real world. This particularly restricts the effective deployment of…
Embedding-based retrieval (EBR), the mainstream approach in information retrieval (IR), aims to help users obtain relevant information and plays a crucial role in retrieval-augmented generation (RAG) techniques of large language models (LLMs). Numerous methods have been proposed to significantly imp…
Numerous advanced Large Language Models (LLMs) now support context lengths up to 128K, and some extend to 200K. Some benchmarks in the generic domain have also followed up on evaluating long-context capabilities. In the medical domain, tasks are distinctive due to the unique contexts and need for do…
The Clinical Terminology Normalization aims at finding standard terms from a given termbase for mentions extracted from clinical texts. However, we found that extracted mentions suffer from the multi-implication problem, especially disease diagnoses. The reason for this is that physicians often use…
Most medical dialogue systems assume that patients have clear goals (seeking a diagnosis, medicine querying, etc.) before medical consultation. However, in many real situations, due to the lack of medical knowledge, it is usually difficult for patients to determine clear goals with all necessary slo…