Generative AI release decisions determine whether system components are made available, but release does not address many other elements that change how users and stakeholders are able to engage with a system. Beyond release, access to system components informs potential risks and benefits. Access refers to practical needs, infrastructurally, technically, and societally, in order to use available components in some way. We deconstruct access along three axes: resourcing, technical usability, and utility. Within each category, a set of variables per system component clarify tradeoffs. For example, resourcing requires access to computing infrastructure to serve model weights. We also compare the accessibility of four high performance language models, two open-weight and two closed-weight, showing similar considerations for all based instead on access variables. Access variables set the foundation for being able to scale or increase access to users; we examine the scale of access and how scale affects ability to manage and intervene on risks. This framework better encompasses the landscape and risk-benefit tradeoffs of system releases to inform system release decisions, research, and policy.

The proliferation of multimodal content on social media presents significant challenges in understanding and moderating complex, context-dependent issues such as misinformation, hate speech, and propaganda. While efforts have been made to develop resources and propose new methods for automatic detection, limited attention has been given to label detection and the generation of explanation-based rationales for predicted labels. To address this challenge, we introduce MemeIntel, an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes in English, making it the first large-scale resource for these tasks. To solve these tasks, we propose a multi-stage optimization approach and train Vision-Language Models (VLMs). Our results demonstrate that this approach significantly improves performance over the base model for both \textbf{label detection} and explanation generation, outperforming the current state-of-the-art with an absolute improvement of ~3% on ArMeme and ~7% on Hateful Memes. For reproducibility and future research, we aim to make the MemeIntel dataset and experimental resources publicly available.

Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs). However, audio understanding and generation are often treated as distinct tasks, hindering the development of truly unified audio-language models. While instruction tuning has demonstrated remarkable success in improving generalization and zero-shot learning across text and vision, its application to audio remains largely unexplored. A major obstacle is the lack of comprehensive datasets that unify audio understanding and generation. To address this, we introduce Audio-FLAN, a large-scale instruction-tuning dataset covering 80 diverse tasks across speech, music, and sound domains, with over 100 million instances. Audio-FLAN lays the foundation for unified audio-language models that can seamlessly handle both understanding (e.g., transcription, comprehension) and generation (e.g., speech, music, sound) tasks across a wide range of audio domains in a zero-shot manner. The Audio-FLAN dataset is available on HuggingFace and GitHub and will be continuously updated.

Rumours in online social media pose significant risks to modern society, motivating the need for better understanding of how they develop. We focus specifically on the interface between emotion and rumours in threaded discourses, building on the surprisingly sparse literature on the topic which has largely focused on emotions within the original rumour posts themselves, and largely overlooked the comparative differences between rumours and non-rumours. In this work, we provide a comprehensive analytical emotion framework, contrasting rumour and non-rumour cases using existing NLP datasets to further understand the emotion dynamics within rumours. Our framework reveals several findings: rumours exhibit more negative sentiment and emotions, including anger, fear and pessimism, while non-rumours evoke more positive emotions; emotions are contagious in online interactions, with rumours facilitate negative emotions and non-rumours foster positive emotions; and based on causal analysis, surprise acts as a bridge between rumours and other emotions, pessimism is driven by sadness and fear, optimism by joy and love.

There has been significant research on propagandistic content detection across different modalities and languages. However, most studies have primarily focused on detection, with little attention given to explanations justifying the predicted label. This is largely due to the lack of resources that provide explanations alongside annotated labels. To address this issue, we propose a multilingual (i.e., Arabic and English) explanation-enhanced dataset, the first of its kind. Additionally, we introduce an explanation-enhanced LLM for both label detection and rationale-based explanation generation. Our findings indicate that the model performs comparably while also generating explanations. We will make the dataset and experimental resources publicly available for the research community.

BRAVE NEW WORLD: Harrison Ford isn't impressed by or afraid of artificial intelligence. In a recent interview with The Wall Street Journal, the "Captain America: Brave New World" star was asked if he was planning on securing control of his likeness from studios, and he brushed off the concern. MAJOR OVERHAUL: OpenAI has announced a set of new measures to combat bias within its suite of products, including ChatGPT. In this photo illustration, the OpenAI logo is seen displayed on a mobile phone screen with ChatGPT logo in the background. HIRING UP: Chipotle Mexican Grill is seeking to hire more workers ahead of "burrito season" – and it is embracing artificial intelligence (AI) tools to help streamline the process.

Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to handle misleading retrievals and often fail to maintain their own reasoning when exposed to conflicting or selectively-framed evidence, making them vulnerable to real-world misinformation. In such real-world retrieval scenarios, misleading and conflicting information is rampant, particularly in the political domain, where evidence is often selectively framed, incomplete, or polarized. However, existing RAG benchmarks largely assume a clean retrieval setting, where models succeed by accurately retrieving and generating answers from gold-standard documents. This assumption fails to align with real-world conditions, leading to an overestimation of RAG system performance. To bridge this gap, we introduce RAGuard, a fact-checking dataset designed to evaluate the robustness of RAG systems against misleading retrievals. Unlike prior benchmarks that rely on synthetic noise, our dataset constructs its retrieval corpus from Reddit discussions, capturing naturally occurring misinformation. It categorizes retrieved evidence into three types: supporting, misleading, and irrelevant, providing a realistic and challenging testbed for assessing how well RAG systems navigate different retrieval information. Our benchmark experiments reveal that when exposed to misleading retrievals, all tested LLM-powered RAG systems perform worse than their zero-shot baselines (i.e., no retrieval at all), highlighting their susceptibility to noisy environments. To the best of our knowledge, RAGuard is the first benchmark to systematically assess RAG robustness against misleading evidence. We expect this benchmark will drive future research toward improving RAG systems beyond idealized datasets, making them more reliable for real-world applications.

Complex claim fact-checking performs a crucial role in disinformation detection. Moreover, evidence redundancy, where nonessential information complicates the verification process, remains a significant issue. To tackle these limitations, we propose Bilateral De fusing V erification ( BiDeV), a novel fact-checking working-flow framework integrating multiple role-played LLMs to mimic the human-expert fact-checking process. BiDeV consists of two main modules: V agueness Defusing identifies latent information and resolves complex relations to simplify the claim, and Redundancy Defusing eliminates redundant content to enhance the evidence quality. Extensive experimental results on two widely used challenging fact-checking benchmarks (Hover and Feverous-s) demonstrate that our BiDeV can achieve the best performance under both gold and open settings. This highlights the effectiveness of BiDeV in handling complex claims and ensuring precise fact-checking 1 . Introduction Fact-checking is crucial for claim verification by collecting relevant evidence and determining their veracity (Guo, Schlichtkrull, and Vlachos 2022).

While there is an increased discourse on large language models (LLMs) like ChatGPT and DeepSeek, there is no comprehensive understanding of how users of online platforms, like Reddit, perceive these models. This is an important omission because public opinion can influence AI development, trust, and future policy. This study aims at analyzing Reddit discussions about ChatGPT and DeepSeek using sentiment and topic modeling to advance the understanding of user attitudes. Some of the significant topics such as trust in AI, user expectations, potential uses of the tools, reservations about AI biases, and ethical implications of their use are explored in this study. By examining these concerns, the study provides a sense of how public sentiment might shape the direction of AI development going forward. The report also mentions whether users have faith in the technology and what they see as its future. A word frequency approach is used to identify broad topics and sentiment trends. Also, topic modeling through the Latent Dirichlet Allocation (LDA) method identifies top topics in users' language, for example, potential benefits of LLMs, their technological applications, and their overall social ramifications. The study aims to inform developers and policymakers by making it easier to see how users comprehend and experience these game-changing technologies.

Traditional Graph Neural Network (GNN) approaches for fake news detection (FND) often depend on auxiliary, non-textual data such as user interaction histories or content dissemination patterns. However, these data sources are not always accessible, limiting the effectiveness and applicability of such methods. Additionally, existing models frequently struggle to capture the detailed and intricate relationships within textual information, reducing their overall accuracy. In order to address these challenges Advanced Text Analysis Graph Neural Network (ATA-GNN) is proposed in this paper. The proposed model is designed to operate solely on textual data. ATA-GNN employs innovative topic modelling (clustering) techniques to identify typical words for each topic, leveraging multiple clustering dimensions to achieve a comprehensive semantic understanding of the text. This multi-layered design enables the model to uncover intricate textual patterns while contextualizing them within a broader semantic framework, significantly enhancing its interpretative capabilities. Extensive evaluations on widely used benchmark datasets demonstrate that ATA-GNN surpasses the performance of current GNN-based FND methods. These findings validate the potential of integrating advanced text clustering within GNN architectures to achieve more reliable and text-focused detection solutions.