GenAI4ELab/papercli-papers huggingface dataset:
REVEAL: Multi-turn Evaluation of Image-Input Harms for Vision LLMs
Authors: Madhur Jindal; Saurabh Deshpande
Venue: IJCAI 2025 (main)

Abstract: Vision Large Language Models (VLLMs) represent a significant advancement in artificial intelligence by integrating image-processing capabilities with textual understanding, thereby enhancing user interactions and expanding application domains. However, their increased complexity introduces novel safety and ethical challenges, particularly in multi-modal and multi-turn conversations. Traditional safety evaluation frameworks, designed for text-based, single-turn interactions, are inadequate for addressing these complexities. To bridge this gap, we introduce the REVEAL (Responsible Evaluation of Vision-Enabled AI LLMs) Framework, a scalable and automated pipeline for evaluating image-input harms in VLLMs. REVEAL includes automated image mining, synthetic adversarial data generation, multi-turn conversational expansion using crescendo attack strategies, and comprehensive harm assessment through evaluators like GPT-4o. We extensively evaluated five state-of-the-art VLLMs, GPT-4o, Llama-3.2, Qwen2-VL, Phi3.5V, and Pixtral, across three important harm categories: sexual harm, violence, and misinformation. Our findings reveal that multi-turn interactions result in significantly higher defect rates compared to single-turn evaluations, highlighting deeper vulnerabilities in VLLMs. Notably, GPT-4o demonstrated the most balanced performance as measured by our Safety-Usability Index (SUI) followed closely by Pixtral. Additionally, misinformation emerged as a critical area requiring enhanced contextual defenses. Llama-3.2 exhibited the highest MT defect rate (16.55%) while Qwen2-VL showed the highest MT refusal rate (19.1%).
An Ethical Dataset from Real-World Interactions Between Users and Large Language Models
Authors: Masahiro Kaneko; Danushka Bollegala; Timothy Baldwin
Venue: IJCAI 2025 (main)

Abstract: Recent studies have demonstrated that Large Language Models (LLMs) have ethical-related problems such as social biases, lack of moral reasoning, and generation of offensive content. The existing evaluation metrics and methods to address these ethical challenges use datasets intentionally created by instructing humans to create instances including ethical problems. Therefore, the data does not sufficiently include comprehensive prompts that users actually provide when using LLM services in everyday contexts and outputs that LLMs generate. There may be different tendencies between unethical instances intentionally created by humans and actual user interactions with LLM services, which could result in a lack of comprehensive evaluation. To investigate the difference, we create Eagle datasets extracted from actual interactions between ChatGPT and users that exhibit social biases, opinion biases, toxicity, and immoral problems. Our experiments show that Eagle captures complementary aspects, not covered by existing datasets proposed for evaluation and mitigation. We argue that using both existing and proposed datasets leads to a more comprehensive assessment of the ethics.
What is Beneath Misogyny: Misogynous Memes Classification and Explanation
Authors: Kushal Kanwar; Dushyant  Singh Chauhan; Gopendra Vikram Singh; Asif Ekbal
Venue: IJCAI 2025 (main)

Abstract: Memes are popular in the modern world and are distributed primarily for entertainment. However, harmful ideologies such as misogyny can be propagated through innocent-looking memes. The detection and understanding of why a meme is misogynous is a research challenge due to its multimodal nature (image and text) and its nuanced manifestations across different societal contexts. We introduce a novel multimodal approach, namely, MM-Misogyny to detect, categorize, and explain misogynistic content in memes. MM-Misogyny processes text and image modalities separately and unifies them into a multimodal context through a cross-attention mechanism. The resulting multimodal context is then easily processed for labeling, categorization, and explanation via a classifier and Large Language Model (LLM). The evaluation of the proposed model is performed on a newly curated dataset (What’s Beneath Misogynous Stereotyping (WBMS)) created by collecting misogynous memes from cyberspace and categorizing them into four categories, namely, Kitchen, Leadership, Working, and Shopping. The model not only detects and classifies misogyny, but also provides a granular understanding of how misogyny operates in operates in domains of life. The results demonstrate the superiority of our approach compared to existing methods. The code and dataset are available at https://github.com/Misogyny.
What is Behind Homelessness Bias? Using LLMs and NLP to Mitigate Homelessness by Acting on Social Stigma
Authors: Jonathan A. Karr Jr.; Emory Smith; Matthew Hauenstein; Georgina Curto; Nitesh V. Chawla
Venue: IJCAI 2025 (main)

Abstract: Bias towards people experiencing homelessness (PEH) is prevalent in online spaces. This project will leverage natural language processing (NLP) and large language models (LLMs) to identify, classify, and measure bias using geolocalized data collected from X (formerly Twitter), Reddit, meeting minutes, and news media across the United States. While public opinion often refers to addictions, criminality, and high levels of welfare spending to justify bias against PEH, we will conduct a comparative study to determine whether racial fractionalization is associated with homelessness bias. The results of the study aim to provide a new path to alleviate homelessness by unveiling the intersectional bias that affects PEH and minority racial groups. During the course of the project, we will deliver a lexicon, compile an annotated database for homelessness and homelessness-racism intersectional (HRI) bias, evaluate LLMs as classifiers of homelessness and HRI bias, develop homelessness and HRI bias metrics, and audit existing LLMs on HRI. In collaboration with non-profits and the city council of South Bend, Indiana, USA, our ultimate goal is to contribute to homelessness alleviation by counteracting social stigma, restoring the dignity and well-being of the persons affected.
Leveraging Artificial Intelligence to Bridge Gaps in Pediatric Oncology Care for Marginalized Spanish-Speaking Communities
Authors: Grigorii Khvatskii; Angelica Garcia Martinez; Deng Pan; Matthew Belcher; Gerónimo Medrano Loera; Dayana Pineda Pérez; Juan Emmanuel Ferrari Muñoz-Ledo; Horacio Márquez-González; Nuno Moniz; Nitesh V. Chawla
Venue: IJCAI 2025 (main)

Abstract: In low-and middle-income countries (LMICs) pediatric cancer patients and their caregivers often suffer from effects of underfunded, fragmented and outdated healthcare systems. One of these effects is a breakdown of communication between hospital staff and caregivers, which is felt stronger among vulnerable populations. Our proposed solution integrates Large Language Models (LLM) and Automatic Speech Recognition (ASR) technologies to enhance communication between caregivers and healthcare providers while integrating community feedback. We combine cutting-edge technology with existing hospital infrastructure to allow for easy deployment and testing. The system will improve access to health, nutrition, and parental care programs, prioritizing caregiver engagement and real-time interaction. Ultimately, our system will pave the way to more equitable access to medical care, and address structural barriers affecting marginalized communities.
Towards the 30 by 30 Kunming-Montreal Global Biodiversity Framework Target: Optimising Graph Connectivity in Constraint-Based Spatial Planning
Authors: Sulian Le Bozec-Chiffoleau; Dimitri Justeau-Allaire; Xavier Lorca; Charles Prud'homme; Gilles Simonin; Philippe Vismara; Philippe Birnbaum; Nicolas Rinck; Nicolas Beldiceanu
Venue: IJCAI 2025 (main)

Abstract: The Kunming-Montreal Global Biodiversity Framework aims to protect 30% of terrestrial, inland water, marine, and coastal ecosystems worldwide, and ensuring that at least 30% of these areas are under effective restoration by 2030. Maintaining and restoring ecological connectivity between natural habitats and protected areas is a key feature of this target. Achieving it will require effective and inclusive spatial planning supported by appropriate decision-support tools. Most spatial planning models address budget as an objective and connectivity as a constraint, formulating problems with Steiner trees. In many real-world cases, such as landscape-scale restoration planning, this formulation is inappropriate when environmental managers seek to optimise connectivity under a budget constraint. This problem was previously addressed with Constraint Programming (CP) and graph variables, but the current approach is severely limited in terms of spatial resolution. In this article, we formalise this problem as the budget-constrained graph connectivity optimisation problem. Based on a real case study: the restoration of forest connectivity in New Caledonia, we illustrate why ``naive'' CP approaches are inefficient. In response, we provide a preprocessing method based on Hanan grids which preserves the existence of at least one optimal solution. Finally, we assess the efficiency of our approach in the New Caledonian case study.
AI-Assisted Triage and Decision Support of Head and Neck Cancer Screening and Diagnosis in Low-Resourced Settings
Authors: Min Hun Lee; Sean Shao Wei Lam; Shaun Xin Hong Liew; Michael Dorosan; Nicholas Graves; Jonas Karlström; Hiang Khoon Tan; Walter Tsong Lee
Venue: IJCAI 2025 (main)

Abstract: The mortality burden of head and neck cancer (HNC) is increasing globally and disproportionately affects people in low-and middle-income countries with limited medical workforce. To address this issue, artificial intelligence (AI) algorithms are increasingly being explored to process medical imaging data, demonstrating competitive performance. However, the clinical adoption of AI remains challenging as clinicians struggle to understand how complex AI works and trust it to use in practice. In addition, AI may not perform well on varying data qualities of endoscopy videos for HNC screening and diagnosis from multiple sites. In this project, our international and interdisciplinary team will collaborate with clinicians from multiple sites (e.g. Singapore, the U.S., and Bangladesh) to collect a diverse, multi-site dataset. In addition, we aim to design and develop computational techniques and practices to improve collaborations between clinicians and AI for the triage and diagnosis of HNC. Specifically, these techniques include a YOLOv5-based glottis detector, a classifier of patient's status using clinical endoscopy videos, uncertainty quantification techniques, and interactive Vision Language Model-based AI explanations, which will enable clinicians to understand AI outputs and provide their inputs to improve AI. After developing our system, we will evaluate the effectiveness of these computational techniques in enabling AI-assisted point-of-care triage and decision-support for HNC, particularly in resource-limited settings.
Deep Reinforcement Learning for Efficient and Fair Allocation of Healthcare Resources
Authors: Yikuan Li; Chengsheng Mao; Kaixuan Huang; Hanyin Wang; Zheng Yu; Mengdi Wang; Yuan Luo
Venue: IJCAI 2025 (main)

Abstract: The scarcity of health care resources, such as ventilators, often leads to the unavoidable consequence of rationing, particularly during public health emergencies or in resource-constrained settings like pandemics. The absence of a universally accepted standard for resource allocation protocols results in governments relying on varying criteria and heuristic-based approaches, often yielding suboptimal and inequitable outcomes. This study addresses the societal challenge of fair and effective critical care resource allocation by leveraging deep reinforcement learning to optimize policy decisions. We propose a transformer-based deep Q-network that integrates individual patient disease progression and interaction effects among patients to enhance allocation decisions. Our method aims to improve both fairness and overall patient outcomes. Experiments using metrics such as normalized survival rates and interracial allocation rate differences demonstrate that our approach significantly reduces excess deaths and achieves more equitable resource allocation compared to severity- and comorbidity-based protocols currently in use. Our findings highlight the potential of deep reinforcement learning to address critical health care challenges.
Mat-Instructions: A Large-Scale Inorganic Material Instruction Dataset for Large Language Models
Authors: Ke Liu; Shangde Gao; Yichao Fu; Xiaoliang Wu; Shuo Tong; Ajitha Rajan; Hao Xu
Venue: IJCAI 2025 (main)

Abstract: Recent advancements in large language models (LLMs) have revolutionized research discovery across various scientific disciplines, including materials science. The discovery of novel materials, particularly crystal materials, is essential for achieving sustainable development goals (SDGs), as they drive breakthroughs in climate change mitigation, clean and affordable energy, and the promotion of industrial innovation. However, unlocking the full potential of LLMs in materials research remains challenging due to the lack of high-quality, diverse, and instruction-based datasets. Such datasets are crucial for guiding these models in understanding and predicting the structure, property, and function of materials across various tasks. To address this limitation, we introduce Mat-Instruction, a large-scale inorganic material instruction dataset, specifically designed to unlock the potential of LLMs in materials science. Extensive experiments on fine-tuning LLaMA with our Mat-Instruction dataset demonstrate its effectiveness in advancing progress for materials science. The code and dataset are available at https://github.com/zjuKeLiu/Mat-Instructions
Expanding Connected Components from Alternative Terminals: Global Optimization for Freshwater Fishes Under the UN's 30x30 Conservation Goal
Authors: Yue Mao; Zhongdi Qu; Imanol Miqueleiz; Aaron Ferber; Sami Wolf; Marc Grimson; Sebastian Heilpern; Felipe S. Pacheco; Alexander S. Flecker; Peter B. McIntyre; Carla P. Gomes
Venue: IJCAI 2025 (main)

Abstract: Climate change and biodiversity loss are among humanity’s most pressing challenges. In 2022, under the auspices of the United Nations, over 190 countries reached a historic agreement to address the alarming loss of biodiversity and restore natural ecosystems. Target 3, often referred to as ``30x30'', seeks to effectively protect and manage 30% of the world’s terrestrial, inland water, coastal, and marine areas by 2030. In this work, we address the UN 30x30 target in the context of global freshwater fish conservation. Freshwater ecosystems are disproportionately unprotected, and their biota are declining at an alarming rate. Our goal is to select new protected areas that protect freshwater fish species as much as possible without exceeding total coverage of 30% of land area. To support this goal, we introduce the Expansion of Connected Components from Alternative Terminals Problem, a graph-based optimization problem that captures ecological priorities and connectivity constraints. We analyze its computational complexity, propose novel integer programming formulations, and develop scalable solution methods. We further evaluate its typical-case complexity under diverse settings and demonstrate that our approach scales to a global real-world scope, encompassing approximately 200,000 freshwater basins and 13,000 species, paving the way for implementing the 30x30 target on a worldwide scale.
Hazard Function Guided Agent-Based Models: A Case Study of Return Migration from Poland to Ukraine
Authors: Zakaria Mehrab; S.S. Ravi; Logan Stundal; Samarth Swarup; Srini Venkatramanan; Bryan Lewis; Henning Mortveit; David Leblang; Madhav V. Marathe
Venue: IJCAI 2025 (main)

Abstract: The Russian invasion of Ukraine in February 2022 has led to the largest forced migration crisis in Europe since World War II, with millions displaced both internally and internationally. Among the displaced, approximately 4.2 million individuals have returned, highlighting the significance of return migration as a critical phase in the migration continuum. Existing studies on return migration are limited in scope, relying on survey-based approaches that suffer from demographic bias, lack of validation against ground truth, and inability to account for uncertainty. We propose a novel computational framework for modeling the return of conflict-induced migrants, using agent-based models (ABMs) and their surrogates. These models are grounded in hazard functions and account for sociopolitical contexts. Our proposed ABMs outperform baseline methods in estimating return migration from Poland to Ukraine by at least 42% and by as much as 57% in terms of normalized root mean squared error (NRMSE). Further, to illustrate the utility of such models for policymakers, we conduct two case studies that estimate the duration of displacement and characterize the demographic breakdown among the returnees.
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
Authors: Benedetta Muscato; Lucia Passaro; Gizem Gezici; Fosca Giannotti
Venue: IJCAI 2025 (main)

Abstract: In the realm of Natural Language Processing (NLP), common approaches for handling human disagreement consist of aggregating annotators' viewpoints to establish a single ground truth. However, prior studies show that disregarding individual opinions can lead to the side-effect of under-representing minority perspectives, especially in subjective tasks, where annotators may systematically disagree because of their preferences. Recognizing that labels reflect the diverse backgrounds, life experiences, and values of individuals, this study proposes a new multi-perspective approach using soft labels to encourage the development of the next generation of perspective-aware models—more inclusive and pluralistic. We conduct an extensive analysis across diverse subjective text classification tasks including hate speech, irony, abusive language, and stance detection, to highlight the importance of capturing human disagreements, often overlooked by traditional aggregation methods. Results show that the multi-perspective approach not only better approximates human label distributions, as measured by Jensen-Shannon Divergence (JSD), but also achieves superior classification performance (higher F1-scores), outperforming traditional approaches. However, our approach exhibits lower confidence in tasks like irony and stance detection, likely due to the inherent subjectivity present in the texts. Lastly, leveraging Explainable AI (XAI), we explore model uncertainty and uncover meaningful insights into model predictions. All implementation details are available at our github repo.
SMILE: A Scale-aware Multiple Instance Learning Method for Multicenter STAS  Lung Cancer Histopathology Diagnosis
Authors: Liangrui Pan; Xiaoyu Li; Yutao Dou; Qiya Song; Jiadi Luo; Qingchun Liang; Shaoliang Peng
Venue: IJCAI 2025 (main)

Abstract: Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse prognostic factors and complex pathological features. Pathologists currently rely on time-consuming manual assessments, which are highly subjective and prone to variation. This highlights the urgent need for automated and precise diagnostic solutions. 2,970 lung cancer tissue slides are comprised from multiple centers, re-diagnosed them, and constructed and publicly released three lung cancer STAS datasets: STAS-CSU (hospital), STAS-TCGA, and STAS-CPTAC. All STAS datasets provide corresponding pathological feature diagnoses and related clinical data. To address the bias, sparse and heterogeneous nature of STAS, we propose an scale-aware multiple instance learning(SMILE) method for STAS diagnosis of lung cancer. By introducing a scale-adaptive attention mechanism, the SMILE can adaptively adjust high-attention instances, reducing over-reliance on local regions and promoting consistent detection of STAS lesions. Extensive experiments show that SMILE achieved competitive diagnostic results on STAS-CSU, diagnosing 251 and 319 STAS samples in CPTAC and TCGA, respectively, surpassing clinical average AUC. The 11 open baseline results are the first to be established for STAS research, laying the foundation for the future expansion, interpretability, and clinical integration of computational pathology technologies. The datasets and code are available at https://github.com/panliangrui/IJCAI25.
Classifying and Tracking International Aid Contribution Towards SDGs
Authors: Sungwon Park; Dongjoon Lee; Kyeongjin Ahn; Yubin Choi; Junho Lee; Meeyoung Cha; Kyung Ryul Park
Venue: IJCAI 2025 (main)

Abstract: International aid is a critical mechanism for promoting economic growth and well-being in developing nations, supporting progress toward the Sustainable Development Goals (SDGs). However, tracking aid contributions remains challenging due to labor-intensive data management, incomplete records, and the heterogeneous nature of aid data. Recognizing the urgency of this challenge, we partnered with government agencies to develop an AI model that complements manual classification and mitigates human bias in subjective interpretation. By integrating SDG-specific semantics and leveraging prior knowledge from language models, our approach enhances classification accuracy and accommodates the diversity of aid projects. When applied to a comprehensive dataset spanning multiple years, our model can reveal hidden trends in the temporal evolution of international development cooperation. Expert interviews further suggest how these insights can empower policymakers with data-driven decision-making tools, ultimately improving aid effectiveness and supporting progress toward SDGs.
CoDiCast: Conditional Diffusion Model for Global Weather Forecasting with Uncertainty Quantification
Authors: Jimeng Shi; Bowen Jin; Jiawei Han; Sundararaman Gopalakrishnan; Giri Narasimhan
Venue: IJCAI 2025 (main)

Abstract: Accurate weather forecasting is critical for science and society. However, existing methods have not achieved the combination of high accuracy, low uncertainty, and high computational efficiency simultaneously. On one hand, traditional numerical weather prediction (NWP) models are computationally intensive because of their complexity. On the other hand, most machine learning-based weather prediction (MLWP) approaches offer efficiency and accuracy but remain deterministic, lacking the ability to capture forecast uncertainty. To tackle these challenges, we propose a conditional diffusion model, CoDiCast, to generate global weather prediction, integrating accuracy and uncertainty quantification at a modest computational cost. The key idea behind the prediction task is to generate realistic weather scenarios at a future time point, conditioned on observations from the recent past. Due to the probabilistic nature of diffusion models, they can be properly applied to capture the uncertainty of weather predictions. Therefore, we accomplish uncertainty quantifications by repeatedly sampling from stochastic Gaussian noise for each initial weather state and running the denoising process multiple times. Experimental results demonstrate that CoDiCast outperforms several existing MLWP methods in accuracy, and is faster than NWP models in inference speed. Our model can generate 6-day global weather forecasts, at 6-hour steps and 5.625-degree latitude-longitude resolutions, for over 5 variables, in about 12 minutes on a commodity A100 GPU machine with 80GB memory. The source code is available at https://github.com/JimengShi/CoDiCast.