Jailbreak attacks craft specific prompts or append adversarial suffixes to prompts, thereby inducing language models to generate harmful or unethical content and bypassing the model’s safety guardrails. With the recent blossom of large language models (LLMs), there’s a growing focus on jailbreak attacks to probe their safety. While current white-box attacks typically focus on meticulously identifying adversarial suffixes for specific models, their effectiveness and efficiency diminish when applied to different LLMs. In this paper, we propose a Monte Carlo Tree Search (MCTS) based Prompt Auto-generation (MPA) method to enhance the effectiveness and efficiency of attacks across various models. MPA automatically searches for and generates adversarial suffixes for valid jailbreak attacks. Specifically, we first identify a series of action candidates that could potentially trick LLMs into providing harmful responses. To streamline the exploration of adversarial suffixes, we design a prior confidence probability for each MCTS node. We then iteratively auto-generate adversarial prompts using the MCTS framework. Extensive experiments on multiple open-source models (like Llama, Gemma, and Mistral) and closed-source models (such as ChatGPT) show that our proposed MPA surpasses existing methods in search efficiency as well as attack effectiveness. The codes are available at https://github.com/KDEGroup/MPA.

Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. This oversight creates an evaluation gap: although humans intuitively recognize how context shapes meme interpretation, Large Vision Language Models (LVLMs) can hardly understand context-dependent meme intent. To address this critical limitation, we introduce MemeReaCon, a novel benchmark specifically designed to evaluate how LVLMs understand memes in their original context. We collected memes from five different Reddit communities, keeping each meme’s image, the post text, and user comments together. We carefully labeled how the text and meme work together, what the poster intended, how the meme is structured, and how the community responded. Our tests with leading LVLMs show a clear weakness: models either fail to interpret critical information in the contexts, or overly focus on visual details while overlooking communicative purpose. MemeReaCon thus serves both as a diagnostic tool exposing current limitations and as a challenging benchmark to drive development toward more sophisticated LVLMs of the context-aware understanding.

Recent advances in large language models have demonstrated remarkable performance on Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions. But they add human bias to the reasoning process and fail to leverage models’ inherent reasoning capabilities. To address these limitations, we present T²: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity. T² leverages the insight that if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply the same strategy to the original question. This insight enables to adoption of concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. T² works through four key steps: decomposing questions into structural elements, generating similar examples with candidate reasoning strategies, evaluating these strategies against multiple criteria, and applying the most appropriate strategy to the original question. Experimental evaluation across seven diverse CQA benchmarks demonstrates that T² not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2%.

Diagnosis based on Electronic Health Records (EHRs) often struggles with data scarcity and privacy concerns. To address these issues, we introduce RareSyn, an innovative data synthesis approach designed to augment and de-identify EHRs, with a focus on rare diseases. The core insight of RareSyn involves using seed EHRs of rare diseases to recall similar records from both common and rare diseases, and then leveraging Large Language Models to substitute the key medical information (e.g., symptoms or examination details) in these records with information from the knowledge graph, thereby generating new EHRs. We first train a transformer Encoder with contrastive learning to integrate various types of medical knowledge. Then, RareSyn engages in iterative processes of recalling similar EHRs, structuring EHRs, revising EHRs, and generating new EHRs until the produced EHRs achieve extensive coverage of the rare disease knowledge. We assess RareSyn based on its utility for diagnosis modeling, the diversity of medical knowledge it incorporates, and the privacy of the synthesized EHRs. Extensive experiments demonstrate its effectiveness in improving disease diagnosis, enhancing diversity, and maintaining privacy.

Distinguishing between extremely similar diseases is a critical and challenging aspect of clinical decision-making. Traditional classification, contrastive learning, and Large Language Models (LLMs) based methods fail to detect the subtle clues necessary for differentiation. This task demands complex reasoning and a variety of tools to identify minor differences and make informed decisions. This paper probes a novel framework that leverages LLMs and a multi-agent system to achieve accurate disease diagnosis through a process of repeated debate and reassessment. The approach aims to identify subtle differences between similar disease candidates. We structure patient information and integrate extensive medical knowledge to guide the analysis towards discerning these differences for precise diagnosis. Comprehensive experiments were conducted on two public datasets and two newly introduced datasets, JarvisD2-Chinese and JarvisD2-English, to validate the effectiveness of our method. The results confirm the efficacy of our approach, demonstrating its potential to enhance diagnostic precision in healthcare.

Automatic evaluation of natural language generation (NLG) tasks has gained extensive research interests, since it can rapidly assess the performance of large language models (LLMs). However, automatic NLG evaluation struggles with medical QA because it fails to focus on the crucial correctness of medical facts throughout the generated text. To address this, this paper introduces a new data structure, imap, designed to capture key information in questions and answers, enabling evaluators to focus on essential details. The imap comprises three components: Query, Constraint, and Inform, each of which is in the form of term-value pairs to represent medical facts in a structural manner. We then introduce imapScore, which compares the corresponding medical term-value pairs in the imap to score generated texts. We utilize GPT-4 to extract imap from questions, human-annotated answers, and generated responses. To mitigate the diversity in medical terminology for fair term-value pairs comparison, we use a medical knowledge graph to assist GPT-4 in determining matches. To compare imapScore with existing NLG metrics, we establish a new benchmark dataset. The experimental results show that imapScore consistently outperforms state-of-the-art metrics, demonstrating an average improvement of 79.8% in correlation with human scores. Furthermore, incorporating imap into n-gram, embedding, and LLM metrics boosts the base versions, increasing correlation with human scores by averages of 89.9%, 81.7%, and 32.6%, respectively.

The bias of disease prediction in Large Language Models (LLMs) is a critical yet underexplored issue, with potential implications for healthcare outcomes and equity. As LLMs increasingly find applications in healthcare, understanding and addressing their biases becomes paramount. This study focuses on this crucial topic, investigating the bias of disease prediction in models such as GPT-4, ChatGPT, and Qwen1.5-72b across gender, age range, and disease judgment behaviors. Utilizing a comprehensive real-clinical health record dataset of over 330,000 entries, we uncover that all three models exhibit distinct biases, indicating a pervasive issue of unfairness. To measure this, we introduce a novel metric–the diagnosis bias score, which reflects the ratio of prediction numbers to label numbers. Our in-depth analysis, based on this score, sheds light on the inherent biases in these models. In response to these findings, we propose a simple yet effective prompt-based solution to alleviate the observed bias in disease prediction with LLMs. This research underscores the importance of fairness in AI, particularly in healthcare applications, and offers a practical approach to enhance the equity of disease prediction models.

Artificial intelligence (AI)-aided disease prediction has gained extensive research interest due to its capability to support clinical decision-making. Existing works mainly formulate disease prediction as a multi-label classification problem and use historical Electronic Medical Records (EMR) to train supervised models. However, in real-world clinics, such purely data-driven approaches pose two main challenges: 1) long tail problem: there are excessive EMRs for common diseases and insufficient EMRs for rare diseases, thus training over an imbalanced data set could result in a biased model that ignores rare diseases in diagnosis; 2) easily misdiagnosed diseases: some diseases can be easily distinguished while others sharing analogous conditions are much more difficult. General classification models without emphasizing easily misdiagnosed diseases may generate incorrect predictions. To tackle these two problems, we propose a Medical Knowledge-Enhanced Contrastive Learning (MKeCL) approach to disease diagnosis in this paper. MKeCL incorporates medical knowledge graphs and medical licensing exams in modeling in order to compensate for the insufficient information on rare diseases; To handle hard-to-diagnose diseases, MKeCL introduces a contrastive learning strategy to separate diseases that are easily misdiagnosed. Moreover, we establish a new benchmark, named Jarvis-D, which contains clinical EMRs collected from various hospitals. Experiments on real clinical EMRs show that the proposed MKeCL outperforms existing disease prediction approaches, especially in the setting of few-shot and zero-shot scenarios.