Austria

Phishing attacks represent a significant cybersecurity threat, necessitating adaptive detection techniques. This study explores few-shot Adaptive Linguistic Prompting (ALP) in detecting phishing webpages through the multimodal capabilities of state-of-the-art large language models (LLMs) such as GPT-4o and Gemini 1.5 Pro. ALP is a structured semantic reasoning method that guides LLMs to analyze textual deception by breaking down linguistic patterns, detecting urgency cues, and identifying manipulative diction commonly found in phishing content. By integrating textual, visual, and URL-based analysis, we propose a unified model capable of identifying sophisticated phishing attempts. Our experiments demonstrate that ALP significantly enhances phishing detection accuracy by guiding LLMs through structured reasoning and contextual analysis. The findings highlight the potential of ALP-integrated multimodal LLMs to advance phishing detection frameworks, achieving an F1-score of 0.93—surpassing traditional approaches. These results establish a foundation for more robust, interpretable, and adaptive linguistic-based phishing detection systems using LLMs.

ACL 2025

Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation. As human interactions increasingly bleed into the large, sometimes opaque, digital realms of social media and the internet, aspects of misinformation also become increasingly difficult to control for, gauge, and study. To address this, we present a simulation environment designed to explore the impact of manipulation on societal-scale interactions and phenomena with a focus on both the real and the digital worlds. By leveraging large language model (LLM)-based multi-agent simulations, we extend the Concordia framework to include several key innovations: (1) integrating a Mastodon server to enable agents to replicate complex social media interactions, (2) an adapter that uses real-world user data to create more grounded agents and social media content; and (3) multi-modal capabilities that enable our agents to interact using both text and images---just as humans do on social media. This hybrid system allows us to model not only public personas in normal agent-to-agent interactions, but also personas and interactions in the context of social media, with the possibility of examining how the interplay influence system outcomes. We aim for more granular and realistic simulations of social scenarios, such as elections, where robustness against manipulation is critical. Our framework supports diverse agent personas, their generation, and tools to analyze belief shifts and interactions in response to disinformation along with other framework features which we are continuing to build upon. We also provide measurement and analysis tools that track simulation dynamics, evaluate metrics to compare settings, and reveal changes to individual and population-level beliefs.

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents

What ethical concerns, if any, do researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Anthology. We extract ethical concern keywords from the statements and show promising results in automating the concern identification process. Through a survey (N=200), we compare the ethical concerns of the corpus to the concerns listed by laypeople and professionals in the field. Finally, we compare our retrieved ethical concerns with existing taxonomies and guidelines, pointing to gaps and actionable insights.

Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements

Natural disasters such as tropical cyclones cause annual devastation and take a heavy so- cial cost, as disadvantaged communities are typ- ically hit hardest. Among these communities are the speakers of minority and low-resource languages, who may not be sufficiently in- formed about incoming weather events to pre- pare. This work presents an analysis of the current state of machine translation for natural disasters in the languages of communities that are threatened by them. Results suggest that commercial systems are promising, and that in-genre fine-tuning data are beneficial.

Take Shelter, Zanmi: Digitally Alerting Cyclone Victims in Their Languages

Cognitive Reframing, a core element of Cognitive Behavioral Therapy (CBT), helps individuals reinterpret negative experiences by finding positive meaning. Recent advances in Large Language Models (LLMs) have demonstrated improved performance through reasoning-based strategies. This inspires a promising direction of leveraging the reasoning capabilities of LLMs to improve CBT and mental reframing by simulating the process of critical thinking, potentially enabling more effective recognition, generation and reframing of cognitive distortions. In this work, we investigate the role of various reasoning methods, including pre-trained reasoning LLMs, such as DeepSeek-R1, and augmented reasoning strategies, such as CoT (Wei et al., 2022) and self-consistency (Wang et al., 2022), in enhancing LLMs’ ability to perform cognitive reframing tasks. We find that augmented reasoning methods, even when applied to older LLMs like GPT-3.5, consistently outperform state-of- the-art pretrained reasoning models such as DeepSeek-R1 (Guo et al., 2025) and o1 (Jaech et al., 2024) on recognizing, generating and reframing unhelpful thoughts.

Does “Reasoning” with Large Language Models Improve Recognizing, Generating and Reframing Unhelpful Thoughts?

The rise of Large Language Models (LLMs) has raised questions about their ability to understand climate-related contexts. Though climate change dominates social media, analyzing its multimodal expressions is understudied, and current tools have failed to determine whether LLMs amplify credible solutions or spread unsubstantiated claims. To address this, we introduce CliME (Climate Change Multimodal Evaluation), a first-of-its-kind multimodal dataset, comprising 2579 Twitter and Reddit posts. The benchmark features a diverse collection of humorous memes and skeptical posts, capturing how these formats distill complex issues into viral narratives that shape public opinion and policy discussions. To systematically evaluate LLM performance, we present the Climate Alignment Quotient (CAQ), a novel metric comprising five distinct dimensions: Articulation, Evidence, Resonance, Transition, and Specificity. Additionally, we propose three analytical lenses: Actionability, Criticality, and Justice, to guide the assessment of LLM-generated climate discourse using CAQ. Our findings, based on the CAQ metric, indicate that while most evaluated LLMs perform relatively well in Criticality and Justice, they consistently underperform on the Actionability axis. Among the models evaluated, Claude 3.7 Sonnet achieves the highest overall performance. We publicly release our code and dataset to foster further research in this domain.

CliME: Evaluating Multimodal Climate Discourse on Social Media and the Climate Alignment Quotient (CAQ)

Despite regulations imposed by nations and social media platforms, e.g. (Government of India, 2021; European Parliament and Council of the European Union, 2022), inter alia, hateful content persists as a significant challenge. Existing approaches primarily rely on reactive measures such as blocking or suspending offensive messages, with emerging strategies focusing on proactive measurements like detoxification and counterspeech. In our work, which we call HatePRISM, we conduct a comprehensive examination of hate speech regulations and strategies from three perspectives: country regulations, social platform policies, and NLP research datasets. Our findings reveal significant inconsistencies in hate speech definitions and moderation practices across jurisdictions and platforms, alongside a lack of alignment with research efforts. Based on these insights, we suggest ideas and research direction for further exploration of a unified framework for automated hate speech moderation incorporating diverse strategies.

HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation

Early detection of disease outbreaks is crucial to ensure timely intervention by the health authorities. Due to the challenges associated with traditional indicator-based surveillance, monitoring informal sources such as online media has become increasingly popular. However, owing to the number of online articles getting published everyday, manual screening of the articles is impractical. To address this, we propose Health Sentinel. It is a multi-stage information extraction pipeline that uses a combination of ML and non-ML methods to extract events–structured information concerning disease outbreaks or other unusual health events–from online articles. The extracted events are made available to the Media Scanning and Verification Cell (MSVC) at the National Centre for Disease Control (NCDC), Delhi for analysis, interpretation and further dissemination to local agencies for timely intervention. From April 2022 till date, Health Sentinel has processed over 300 million news articles and identified over 95,000 unique health events across India of which over 3,500 events were shortlisted by the public health experts at NCDC as potential outbreaks.

Health Sentinel: An AI Pipeline For Real-time Disease Outbreak Detection

The integration of large language models (LLMs) into mental health applications offers promising opportunities for positive social impact. However, it also presents critical risks. While previous studies have often addressed these challenges and risks individually, a broader and multi-dimensional approach is still lacking. In this paper, we introduce a taxonomy of the main challenges related to the use of LLMs for mental health and propose a structured, comprehensive research agenda to mitigate them. We emphasize the need for explainable, emotionally aware, culturally sensitive, and clinically aligned systems, supported by continuous monitoring and human oversight. By placing our work within the broader context of natural language processing (NLP) for positive impact, this research contributes to ongoing efforts to ensure that technological advances in NLP responsibly serve vulnerable populations, fostering a future where mental health solutions improve rather than endanger well-being.

Guardians of Trust: Risks and Opportunities for LLMs in Mental Health

Green industrial policies (GIPs) are government interventions that support environmentally sustainable economic growth through targeted incentives, regulations, and investments in clean technologies. As the backbone of climate mitigation and adaptation, GIPs deserve systematic documentation and analysis. However, two major hurdles impede this systematic documentation. First, unlike other climate policy documents, such as Nationally Determined Contributions (NDCs) which are centrally curated, GIPs are scattered across numerous government legislation and policy announcements. Second, extracting information from these diverse documents is expensive when relying on expert annotation. We address this gap by proposing \textit{GreenSpyder}, an LLM-based workflow that actively monitors, classifies, and annotates GIPs from open-source information. As a demonstration, we benchmark LLM performance in classifying and annotating GIPs on a small expert-curated dataset. Our results show that LLMs can be quite effective for classification and coarse annotation tasks, though they still need improvement for more nuanced classification. Finally, as a real-world application, we apply \textit{GreenSpyder} to U.S. Legislative Records from the 117th Congress, paving the way for more comprehensive LLM-based GIP documentation in the future.

Tracking Green Industrial Policies with LLMs: A Demonstration

Transformer-based Job Ranking Systems (JRSs) are vulnerable to societal biases inherited in unbalanced datasets. These biases often manifest as unjust job rankings, particularly disadvantaging candidates of different genders. Most bias mitigation techniques leverage candidates’ gender and align gender distributions within the embeddings of JRSs to mitigate bias. While such methods effectively align distributional properties and make JRSs agnostic to gender, they frequently fall short in addressing empirical fairness metrics, such as the performance gap across genders. In this study, we shift our attention from candidate gender to mitigate bias based on gendered language in job advertisements. We propose a novel neutrality score based on automatically discovered biased words in job ads and use it to re-rank the model’s decisions. We evaluate our method by comparing it with different bias mitigation strategies and empirically demonstrate that our proposed method not only improves fairness but can also enhance the model’s performance.


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