Integrating Large Language Models (LLMs) with existing Knowledge Graph (KG) databases presents a promising avenue for enhancing LLMs’ efficacy and mitigating their “hallucinations”. Given that most KGs reside in graph databases accessible solely through specialized query languages (e.g., Cypher), it is critical to connect LLMs with KG databases by automating the translation of natural language into Cypher queries (termed as “Text2Cypher” task). Prior efforts tried to bolster LLMs’ proficiency in Cypher generation through Supervised Fine-Tuning (SFT). However, these explorations are hindered by the lack of annotated datasets of Query-Cypher pairs, resulting from the labor-intensive and domain-specific nature of such annotation. In this study, we propose SyntheT2C, a methodology for constructing a synthetic Query-Cypher pair dataset, comprising two distinct pipelines: (1) LLM-based prompting and (2) template-filling. SyntheT2C is applied to two medical KG databases, culminating in the creation of a synthetic dataset, MedT2C. Comprehensive experiments demonstrate that the MedT2C dataset effectively enhances the performance of backbone LLMs on Text2Cypher task via SFT. Both the SyntheT2C codebase and the MedT2C dataset will be released.

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 of patients’ conditions. We argue that single-turn question-answering tasks such as MultiMedQA are insufficient for evaluating LLMs’ medical consultation abilities. To address this limitation, we developed an evaluation benchmark called Multi-turn Medical Dialogue Evaluation (MMD-Eval), specifically designed to evaluate the proactive communication and diagnostic capabilities of medical LLMs during consultations. Considering the high cost and potential for hallucinations in LLMs, we innovatively trained a task-oriented dialogue system to simulate patients engaging in dialogues with the medical LLMs using our structured medical records dataset. This approach enabled us to generate multi-turn dialogue data. Subsequently, we evaluate the communication skills and medical expertise of the medical LLMs. All resources associated with this study will be made publicly available.

Integrating information from various reference databases is a major challenge for Retrieval-Augmented Generation (RAG) systems because each knowledge source adopts a unique data structure and follows different conventions. Retrieving from multiple knowledge sources with one fixed strategy usually leads to under-exploitation of information. To mitigate this drawback, inspired by Mix-of-Expert, we introduce Mix-of-Granularity (MoG), a method that dynamically determines the optimal granularity of a knowledge source based on input queries using a router. The router is efficiently trained with a newly proposed loss function employing soft labels. We further extend MoG to MoG-Graph (MoGG), where reference documents are pre-processed as graphs, enabling the retrieval of distantly situated snippets. Experiments demonstrate that MoG and MoGG effectively predict optimal granularity levels, significantly enhancing the performance of the RAG system in downstream tasks. The code of both MoG and MoGG will be made public.

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 explore the possibility of accomplishing this task through a tuning-free paradigm using powerful Large Language Models (LLMs), hoping to address the costs of re-training due to discrepancies of both standard termbases and annotation protocols. Another major obstacle in this task is that both mentions and terms are short texts. Short texts contain an insufficient amount of information that can introduce ambiguity, especially in a biomedical context. Therefore, besides using the advanced embedding model, we implement a Retrieval-Augmented Generation (RAG) based knowledge card generation module. This module introduces an LLM agent that expands the short texts into accurate, harmonized, and more informative descriptions using a search engine and a domain knowledge base. Furthermore, we present an innovative tuning-free agent collaboration framework for the biomedical terminology normalization task in social media. By leveraging the internal knowledge and the reasoning capabilities of LLM, our framework conducts more sophisticated recall, ranking and re-ranking processes with the collaboration of different LLM agents. Experimental results across multiple datasets indicate that our approach exhibits competitive performance. We release our code and data on the github repository JOHNNY-fans/RankNorm.

This paper surveys and organizes research works of medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshape medical dialog systems’ foundation.Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists grand challenges of medical dialog systems, especially of large language models.

While dominant in abstractive summarization, transformer-based language models with the standard maximum likelihood estimation (MLE) training remain challenged by two discrepancies: the misalignment between token-level training and sequence-level evaluation, and the divergence between teacher-forcing training manner and auto-regressive generation behavior. Recent studies have shown that sequence-level contrastive learning, which utilizes the quality differences between multiple summaries as prior information, can effectively mitigate these issues. However, as certain evaluation metrics often determine the contrastive signals in existing methods, this leads to the model performance aligning with the preferences of these metrics being limited by the evaluation capabilities of these metrics. Inspired by prior works that treat the evaluation of generated text as a text generation problem, we propose a generative evaluation-driven contrastive learning framework, which leverages the semantic understanding capabilities of the abstractive model itself to evaluate summary in reference-based settings. In this way, our method establishes a connection between the model’s reference-based evaluation and reference-free generation scenarios, allowing them to share the benefits of model capability enhancements. Extensive experiments on four summarization datasets demonstrate that our method outperforms the previous state-of-the-art regarding comprehensive performance. Various empirical analyses further substantiate the effectiveness of our method.

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 slots. In this paper, we identify this challenge as how to construct medical consultation dialogue systems to help patients clarify their goals. For further study, we create a novel human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering four dialogue types: task-oriented dialogue for diagnosis, recommendation, QA, and chitchat. MidMed covers four departments (otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,309 dialogues. Furthermore, we build benchmarking baselines on MidMed and propose an instruction-guiding medical dialogue generation framework, termed InsMed, to handle mixed-type dialogues. Experimental results show the effectiveness of InsMed.

Most Transformer based abstractive summarization systems have a severe mismatch between training and inference, i.e., exposure bias. From diverse perspectives, we introduce a simple multi-level contrastive learning framework for abstractive summarization (SimMCS) and a tailored sparse decoder self-attention pattern (SDSA) to bridge the gap between training and inference to improve model performance. Compared with previous contrastive objectives focusing only on the relative order of probability mass assigned to non-gold summaries, SimMCS additionally takes their absolute positions into account, which guarantees that the relatively high-quality (positive) summaries among them could be properly assigned high probability mass, and further enhances the capability of discriminating summary quality beyond exploiting potential artifacts of specific metrics. SDSA simulates the possible inference scenarios of deviation in the training phase to get closer to the ideal paradigm. Our approaches outperform the previous state-of-the-art results on two summarization datasets while just adding fairly low overhead. Further empirical analysis shows our model preserves the advantages of prior contrastive methods and possesses strong few-shot learning ability.