Large language models (LLMs) are reported to be partial to certain cultures owing to the training data dominance from the English corpora. Since multilingual cultural data are often expensive to collect, existing efforts handle this by prompt engineering or culture-specific pre-training. However, they might overlook the knowledge deficiency of low-resource culture and require extensive computing resources. In this paper, we propose CultureLLM, a cost-effective solution to incorporate cultural differences into LLMs. CultureLLM adopts World Value Survey (WVS) as seed data and generates semantically equivalent training data via the proposed semantic data augmentation. Using only 50 seed samples from WVS with augmented data, we fine-tune culture-specific LLMs and one unified model (CultureLLM-One) for 9 cultures covering rich and low-resource languages. Extensive experiments on 60 culture-related datasets demonstrate that CultureLLM significantly outperforms various counterparts such as GPT-3.5 (by 8.1%) and Gemini Pro (by 9.5%) with comparable performance to GPT-4 or even better. Our human study shows that the generated samples are semantically equivalent to the original samples, providing an effective solution for LLMs augmentation. Code is released at https://github.com/Scarelette/CultureLLM.

Elon Musk's attorneys filed for an injunction against OpenAI and Microsoft on Friday, accusing them of anticompetitive practices. He wants to stop OpenAI's conversion to a for-profit company. Musk first sued OpenAI earlier this year for allegedly violating its founding mission of building AI "for the benefit of humanity," but he withdrew the lawsuit a few months later. He filed another lawsuit against OpenAI in a California federal court in August. The third time's the charm and all: Musk's new motion accuses OpenAI and Microsoft of telling investors not to fund OpenAI's competitors, such as Musk's xAI, of "benefitting from wrongfully obtained competitively sensitive information or coordination" through its relationship with Microsoft.

Artificial Intelligence (AI) is transforming diverse societal domains, raising critical questions about its risks and benefits and the misalignments between public expectations and academic visions. This study examines how the general public (N=1110) -- people using or being affected by AI -- and academic AI experts (N=119) -- people shaping AI development -- perceive AI's capabilities and impact across 71 scenarios, including sustainability, healthcare, job performance, societal divides, art, and warfare. Participants evaluated each scenario on four dimensions: expected probability, perceived risk and benefit, and overall sentiment (or value). The findings reveal significant quantitative differences: experts anticipate higher probabilities, perceive lower risks, report greater utility, and express more favorable sentiment toward AI compared to the non-experts. Notably, risk-benefit tradeoffs differ: the public assigns risk half the weight of benefits, while experts assign it only a third. Visual maps of these evaluations highlight areas of convergence and divergence, identifying potential sources of public concern. These insights offer actionable guidance for researchers and policymakers to align AI development with societal values, fostering public trust and informed governance.

The AI safety literature is full of examples of powerful AI agents that, in blindly pursuing a specific and usually narrow objective, ends up with unacceptable and even catastrophic collateral damage to others. In this paper, we consider the problem of social harms that can result from actions taken by learning and utility-maximising agents in a multi-agent environment. The problem of measuring social harms or impacts in such multi-agent settings, especially when the agents are artificial generally intelligent (AGI) agents, was listed as an open problem in Everitt et al, 2018. We attempt a partial answer to that open problem in the form of market-based mechanisms to quantify and control the cost of such social harms. The proposed setup captures many well-studied special cases and is more general than existing formulations of multi-agent reinforcement learning with mechanism design in two ways: (i) the underlying environment is a history-based general reinforcement learning environment like in AIXI; (ii) the reinforcement-learning agents participating in the environment can have different learning strategies and planning horizons. To demonstrate the practicality of the proposed setup, we survey some key classes of learning algorithms and present a few applications, including a discussion of the Paperclips problem and pollution control with a cap-and-trade system.

The diffusion model has gained significant attention due to its remarkable data generation ability in fields such as image synthesis. However, its strong memorization and replication abilities with respect to the training data also make it a prime target for copyright infringement attacks. This paper provides an in-depth analysis of the spatial similarity of replication in diffusion model and leverages this key characteristic to design a method for detecting poisoning data. By employing a joint assessment of spatial-level and feature-level information from the detected segments, we effectively identify covertly dispersed poisoned samples. Building upon detected poisoning data, we propose a novel defense method specifically targeting copyright infringement attacks by introducing a protection constraint term into the loss function to mitigate the impact of poisoning. Extensive experimental results demonstrate that our approach achieves an average F1 score of 0.709 in detecting copyright infringement backdoors, resulting in an average increase of 68.1% in First-Attack Epoch (FAE) and an average decrease of 51.4% in Copyright Infringement Rate (CIR) of the poisoned model, effectively defending against copyright infringement. Additionally, we introduce the concept of copyright feature inversion, which aids in determining copyright responsibility and expands the application scenarios of defense strategies.

Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, the paper employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this paper addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.

With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attention. However, prior approaches mainly depend on document-level comparisons using basic indicators like citations due to the lack of facet-level relevance datasets; yet, this limits their use to citation-based domains and fails to capture the intricacies of facet constraints. In this paper, we propose a multi-facet blending (FaBle) augmentation method, which exploits modularity by decomposing and recomposing to explicitly synthesize facet-specific training sets. We automatically decompose documents into facet units and generate (ir)relevant pairs by leveraging LLMs' intrinsic distinguishing capabilities; then, dynamically recomposing the units leads to facet-wise relevance-informed document pairs. Our modularization eliminates the need for pre-defined facet knowledge or labels. Further, to prove the FaBle's efficacy in a new domain beyond citation-based scientific paper retrieval, we release a benchmark dataset for educational exam item QBE. FaBle augmentation on 1K documents remarkably assists training in obtaining facet conditional embeddings.

Unequal access to costly datasets essential for empirical research has long hindered researchers from disadvantaged institutions, limiting their ability to contribute to their fields and advance their careers. Recent breakthroughs in Large Language Models (LLMs) have the potential to democratize data access by automating data collection from unstructured sources. We develop and evaluate a novel methodology using GPT-4o-mini within a Retrieval-Augmented Generation (RAG) framework to collect data from corporate disclosures. Our approach achieves human-level accuracy in collecting CEO pay ratios from approximately 10,000 proxy statements and Critical Audit Matters (CAMs) from more than 12,000 10-K filings, with LLM processing times of 9 and 40 minutes respectively, each at a cost under $10. This stands in stark contrast to the hundreds of hours needed for manual collection or the thousands of dollars required for commercial database subscriptions. To foster a more inclusive research community by empowering researchers with limited resources to explore new avenues of inquiry, we share our methodology and the resulting datasets.

Loneliness, or the lack of fulfilling relationships, significantly impacts a person's mental and physical well-being and is prevalent worldwide. Previous research suggests that large language models (LLMs) may help mitigate loneliness. However, we argue that the use of widespread LLMs like ChatGPT is more prevalent--and riskier, as they are not designed for this purpose. To explore this, we analysed user interactions with ChatGPT, particularly those outside of its marketed use as task-oriented assistant. In dialogues classified as lonely, users frequently (37%) sought advice or validation, and received good engagement. However, ChatGPT failed in sensitive scenarios, like responding appropriately to suicidal ideation or trauma. We also observed a 35% higher incidence of toxic content, with women being 22 times more likely to be targeted than men. Our findings underscore ethical and legal questions about this technology, and note risks like radicalisation or further isolation. We conclude with recommendations for research and industry to address loneliness.

The world of artificial intelligence (AI) is increasingly penetrating all aspects of our personal and professional lives. This proliferation of AI tools and applications are being met with a mixture of excitement, scepticism and even dread [78]. Excitement at the seemingly endless potential of AI applications such as LLMs, especially when they are integrated "within broader systems" [13], scepticism as the realisation dawns that LLMs are in fact fallible as evidenced by hallucinations and hence not the golden bullet that can solve all problems [19, 21], and a feeling of dread for those who believe that LLMs and AI have the potential to detrimentally impact our lives and make people redundant [78]. The ability of some LLMs to pass Theory of Mind (ToM) [64][32] and Turing Tests [7][42] suggests support for the Computational Theory of Mind (CTM), that cognition may be substrate independent. These findings challenge biological essentialism and open new avenues for creating sophisticated AI systems capable of human-like reasoning and interaction.