Abuse reporting services collect reports about abuse victims have suffered. Accurate classification of the submitted reports is fundamental to analyzing the prevalence and financial impact of different abuse types (e.g., sextortion, investment, romance). Current classification approaches are problematic because they require the reporter to select the abuse type from a list, assuming the reporter has the necessary experience for the classification, which we show is frequently not the case, or require manual classification by analysts, which does not scale. To address these issues, this paper presents a novel approach to classify cryptocurrency abuse reports automatically. We first build a taxonomy of 19 frequently reported abuse types. Given as input the textual description written by the reporter, our classifier leverages a large language model (LLM) to interpret the text and assign it an abuse type in our taxonomy. We collect 290K cryptocurrency abuse reports from two popular reporting services: BitcoinAbuse and BBB's ScamTracker. We build ground truth datasets for 20K of those reports and use them to evaluate three designs for our LLM-based classifier and four LLMs, as well as a supervised ML classifier used as a baseline. Our LLM-based classifier achieves a precision of 0.92, a recall of 0.87, and an F1 score of 0.89, compared to an F1 score of 0.55 for the baseline. We demonstrate our classifier in two applications: providing financial loss statistics for fine-grained abuse types and generating tagged addresses for cryptocurrency analysis platforms.

Artificial Intelligence (AI) techniques, particularly machine learning techniques, are rapidly transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach, focusing on how it empowers human decision-making in complex environments. While trust and explainability continue to pose significant challenges, our exploration focuses on the potential of AI-driven HAT to transform tactical operations. By improving situational awareness and supporting more informed decision-making, AI-driven HAT can enhance the effectiveness and safety of such operations. To this end, we propose a comprehensive framework that addresses the key components of AI-driven HAT, including trust and transparency, optimal function allocation between humans and AI, situational awareness, and ethical considerations. The proposed framework can serve as a foundation for future research and development in the field. By identifying and discussing critical research challenges and knowledge gaps in this framework, our work aims to guide the advancement of AI-driven HAT for optimizing tactical operations. We emphasize the importance of developing scalable and ethical AI-driven HAT systems that ensure seamless human-machine collaboration, prioritize ethical considerations, enhance model transparency through Explainable AI (XAI) techniques, and effectively manage the cognitive load of human operators.

Large language models (LLMs; Jiang et al., 2023; Touvron et al., 2023; Dubey et al., 2024; Achiam et al., 2023) have demonstrated remarkable performance on numerous downstream natural language processing (NLP) tasks, leading to their widespread integration into various decision-making processes (Bommasani et al., 2021; Band et al., 2024; Zhou et al., 2024). However, even with significant increases in model size and the expansion of training datasets, it remains infeasible for LLMs to encode all possible knowledge within their parameters. As a result, the outputs produced by LLMs may not consistently be reliable for important human decision-making processes, potentially overlooking key or hidden details. Additionally, LLMs frequently provide inaccurate or misleading information with a high degree of confidence, a phenomenon referred to as hallucination (Zhuo et al., 2023; Papamarkou et al., 2024), which can lead humans to make flawed decisions. In addition, Zhou et al. (2024) have empirically demonstrated that human users often over-rely on LLM outputs during decision-making processes, and this over-reliance tends to increase in proportion to the model's confidence.

Education is being transformed by rapid advances in Artificial Intelligence (AI), including emerging Generative Artificial Intelligence (GAI). Such technology can significantly support academics and students by automating monotonous tasks and making personalised suggestions. However, despite the potential of the technology, there are significant concerns regarding AI misuse, particularly by students in assessments. There are two schools of thought: one advocates for a complete ban on it, while the other views it as a valuable educational tool, provided it is governed by a robust usage policy. This contradiction clearly indicates a major policy gap in academic practices, and new policies are required to uphold academic standards while enabling staff and students to benefit from technological advancements. We surveyed 117 academics from three countries (UK, UAE, and Iraq), and identified that most academics retain positive opinions regarding AI in education. For example, the majority of experienced academics do not favour complete bans, and they see the potential benefits of AI for students, teaching staff, and academic institutions. Importantly, academics specifically identified the particular benefits of AI for autonomous assessment (71.79% of respondents agreed). Therefore, for the first time, we propose a novel AI framework for autonomously evaluating students' work (e.g., reports, coursework, etc.) and automatically assigning grades based on their knowledge and in-depth understanding of the submitted content. The survey results further highlight a significant lack of awareness of modern AI-based tools (e.g., ChatGPT) among experienced academics, a gap that must be addressed to uphold educational standards.

Autonomous weapons systems (AWS) change the way humans make decisions, the effect of those decisions and who is accountable for decisions made. We must remain vigilant, informed and human-centred as we tackle our deliberations on developing norms regarding their development, use and justification. Ways to enhance compliance in international humanitarian law (IHL) include: Training weapons decision makers in IHL; developing best practice in weapons reviews including requirements for industry to ensure that any new weapon, means or method of warfare is capable of being used lawfully; develop human-centred test and evaluation methods; invest in digital infrastructure to increase knowledge of the civilian environment in a conflict and its dynamics; invest in research on the real effects and consequences of civilian harms to the achievement of military and political objectives; improve secure communications between stakeholders in a conflict; and finally to upskill governments and NGOs in what is technically achievable with emerging technologies so that they can contribute to system requirements, test and evaluation protocols and operational rules of use and engagement. Governments are responsible for setting requirements for weapons systems. They are responsible for driving ethicality as well as lethality. Governments can require systems to be made and used to better protect civilians and protected objects. The UN can advocate for compliance with IHL, human rights, human-centred use of weapons systems and improved mechanisms to monitor and trace military decision making including those decisions affected by autonomous functionality.

The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a `low-resource language.' To understand how NLP papers define and study `low resource' languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speech-processing conferences that mention the keyword `low-resource.' Based on our analysis, we show how several interacting axes contribute to `low-resourcedness' of a language and why that makes it difficult to track progress for each individual language. We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource.

As language models (LMs) become integral to fields like healthcare, law, and journalism, their ability to differentiate between fact, belief, and knowledge is essential for reliable decision-making. Failure to grasp these distinctions can lead to significant consequences in areas such as medical diagnosis, legal judgments, and dissemination of fake news. Despite this, current literature has largely focused on more complex issues such as theory of mind, overlooking more fundamental epistemic challenges. This study systematically evaluates the epistemic reasoning capabilities of modern LMs, including GPT-4, Claude-3, and Llama-3, using a new dataset, KaBLE, consisting of 13,000 questions across 13 tasks. Our results reveal key limitations. First, while LMs achieve 86% accuracy on factual scenarios, their performance drops significantly with false scenarios, particularly in belief-related tasks. Second, LMs struggle with recognizing and affirming personal beliefs, especially when those beliefs contradict factual data, which raises concerns for applications in healthcare and counseling, where engaging with a person's beliefs is critical. Third, we identify a salient bias in how LMs process first-person versus third-person beliefs, performing better on third-person tasks (80.7%) compared to first-person tasks (54.4%). Fourth, LMs lack a robust understanding of the factive nature of knowledge, namely, that knowledge inherently requires truth. Fifth, LMs rely on linguistic cues for fact-checking and sometimes bypass the deeper reasoning. These findings highlight significant concerns about current LMs' ability to reason about truth, belief, and knowledge while emphasizing the need for advancements in these areas before broad deployment in critical sectors.

Conversational systems must be robust to user interactions that naturally exhibit diverse conversational traits. Capturing and simulating these diverse traits coherently and efficiently presents a complex challenge. This paper introduces Multi-Trait Adaptive Decoding (mTAD), a method that generates diverse user profiles at decoding-time by sampling from various trait-specific Language Models (LMs). mTAD provides an adaptive and scalable approach to user simulation, enabling the creation of multiple user profiles without the need for additional fine-tuning. By analyzing real-world dialogues from the Conversational Task Assistant (CTA) domain, we identify key conversational traits and developed a framework to generate profile-aware dialogues that enhance conversational diversity. Experimental results validate the effectiveness of our approach in modeling single-traits using specialized LMs, which can capture less common patterns, even in out-of-domain tasks. Furthermore, the results demonstrate that mTAD is a robust and flexible framework for combining diverse user simulators.

Scientific discovery contributes largely to human society's prosperity, and recent progress shows that LLMs could potentially catalyze this process. However, it is still unclear whether LLMs can discover novel and valid hypotheses in chemistry. In this work, we investigate this central research question: Can LLMs automatically discover novel and valid chemistry research hypotheses given only a chemistry research background (consisting of a research question and/or a background survey), without limitation on the domain of the research question? After extensive discussions with chemistry experts, we propose an assumption that a majority of chemistry hypotheses can be resulted from a research background and several inspirations. With this key insight, we break the central question into three smaller fundamental questions. In brief, they are: (1) given a background question, whether LLMs can retrieve good inspirations; (2) with background and inspirations, whether LLMs can lead to hypothesis; and (3) whether LLMs can identify good hypotheses to rank them higher. To investigate these questions, we construct a benchmark consisting of 51 chemistry papers published in Nature, Science, or a similar level in 2024 (all papers are only available online since 2024). Every paper is divided by chemistry PhD students into three components: background, inspirations, and hypothesis. The goal is to rediscover the hypothesis, given only the background and a large randomly selected chemistry literature corpus consisting the ground truth inspiration papers, with LLMs trained with data up to 2023. We also develop an LLM-based multi-agent framework that leverages the assumption, consisting of three stages reflecting the three smaller questions. The proposed method can rediscover many hypotheses with very high similarity with the ground truth ones, covering the main innovations.

Text-to-image (T2I) diffusion models are popular for introducing image manipulation methods, such as editing, image fusion, inpainting, etc. At the same time, image-to-video (I2V) and text-to-video (T2V) models are also built on top of T2I models. We present Kandinsky 3, a novel T2I model based on latent diffusion, achieving a high level of quality and photorealism. The key feature of the new architecture is the simplicity and efficiency of its adaptation for many types of generation tasks. We extend the base T2I model for various applications and create a multifunctional generation system that includes text-guided inpainting/outpainting, image fusion, text-image fusion, image variations generation, I2V and T2V generation. We also present a distilled version of the T2I model, evaluating inference in 4 steps of the reverse process without reducing image quality and 3 times faster than the base model. We deployed a user-friendly demo system in which all the features can be tested in the public domain. Additionally, we released the source code and checkpoints for the Kandinsky 3 and extended models. Human evaluations show that Kandinsky 3 demonstrates one of the highest quality scores among open source generation systems.