-- Prompt engineering has emerged as a useful way studying the algorithmic tendencies and biases of large language models (LLMs). Meanwhile c reatives and academics have leveraged LLMs to develop creative works and explore the boundaries of their writing capabilities through text - generation and code. This study suggests that creative text prompting, specifically "Poetry Prompt Patterns," may be a useful addition to the prompt engineer's toolbox, and outlines the process by which this approach may be taken. Then, the paper uses poetic prompts to assess three models' descriptions and evaluations of a renowned poet and test the consequences of models' willingness to adapt or rewrite original creative works for presumed audiences. Since the release of public - facing chat - style large language model (LLM) natural language generators (NLGs) like ChatGPT and Claude, public debate has acknowledged their great potential for creativity, as well as the ways in which they can be leveraged to make representations that don't reflect reality.

Large language models (LLMs) have emerged as a powerful technology, and thus, we have seen widespread adoption and use on software engineering teams. Most often, LLMs are designed as "general purpose" technologies meant to represent the general population. Unfortunately, this often means alignment with predominantly Western Caucasian narratives and misalignment with other cultures and populations that engage in collaborative innovation. In response to this misalignment, there have been recent efforts centered on the development of "culturally-informed" LLMs, such as ChatBlackGPT, that are capable of better aligning with historically marginalized experiences and perspectives. Despite this progress, there has been little effort aimed at supporting our ability to develop and evaluate culturally-informed LLMs. A recent effort proposed an approach for developing a national alignment benchmark that emphasizes alignment with national social values and common knowledge. However, given the range of cultural identities present in the United States (U.S.), a national alignment benchmark is an ineffective goal for broader representation. To help fill this gap in this US context, we propose a replication study that translates the process used to develop KorNAT, a Korean National LLM alignment benchmark, to develop CIVIQ, a Cultural Intelligence and Values Inference Quality benchmark centered on alignment with community social values and common knowledge. Our work provides a critical foundation for research and development aimed at cultural alignment of AI technologies in practice.

This study presents a comprehensive analysis of public sentiment toward traffic management policies in Knoxville, Tennessee, utilizing social media data from Twitter and Reddit platforms. We collected and analyzed 7906 posts spanning January 2022 to December 2023, employing Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling. Our findings reveal predominantly negative sentiment, with significant variations across platforms and topics. Twitter exhibited more negative sentiment compared to Reddit. Topic modeling identified six distinct themes, with construction-related topics showing the most negative sentiment while general traffic discussions were more positive. Spatiotemporal analysis revealed geographic and temporal patterns in sentiment expression. The research demonstrates social media's potential as a real-time public sentiment monitoring tool for transportation planning and policy evaluation.

Background: Text-to-image generation models are widely used across numerous domains. Among these models, Stable Diffusion (SD) - an open-source text-to-image generation model - has become the most popular, producing over 12 billion images annually. However, the widespread use of these models raises concerns regarding their social and environmental sustainability. Aims: To reduce the harm that SD models may have on society and the environment, we introduce SustainDiffusion, a search-based approach designed to enhance the social and environmental sustainability of SD models. Method: SustainDiffusion searches the optimal combination of hyperparameters and prompt structures that can reduce gender and ethnic bias in generated images while also lowering the energy consumption required for image generation. Importantly, SustainDiffusion maintains image quality comparable to that of the original SD model. Results: We conduct a comprehensive empirical evaluation of SustainDiffusion, testing it against six different baselines using 56 different prompts. Our results demonstrate that SustainDiffusion can reduce gender bias in SD3 by 68%, ethnic bias by 59%, and energy consumption (calculated as the sum of CPU and GPU energy) by 48%. Additionally, the outcomes produced by SustainDiffusion are consistent across multiple runs and can be generalised to various prompts. Conclusions: With SustainDiffusion, we demonstrate how enhancing the social and environmental sustainability of text-to-image generation models is possible without fine-tuning or changing the model's architecture.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but controlling their behavior reliably remains challenging, especially in open-ended generation settings. This paper introduces a novel supervised steering approach that operates in sparse, interpretable representation spaces. We employ sparse autoencoders (SAEs) to obtain sparse latent representations that aim to disentangle semantic attributes from model activations. Then we train linear classifiers to identify a small subspace of task-relevant dimensions in latent representations. Finally, we learn supervised steering vectors constrained to this subspace, optimized to align with target behaviors. Experiments across sentiment, truthfulness, and political polarity steering tasks with multiple LLMs demonstrate that our supervised steering vectors achieve higher success rates with minimal degradation in generation quality compared to existing methods. Further analysis reveals that a notably small subspace is sufficient for effective steering, enabling more targeted and interpretable interventions. Our implementation is publicly available at https://github.com/Ineedanamehere/SAE-SSV.

Judge puts a one-year limit on Google's contracts for default search placement It comes after the September ruling that Google will not have to sell off Chrome, but must make changes. A federal judge has expanded on the remedies decided for the Department of Justice's antitrust case against Google, ruling in favor of putting a one-year limit on the contracts that make Google's search and AI services the default on devices, reports. Judge Amit Mehta's ruling on Friday means Google will have to renegotiate these contacts every year, which would create a fairer playing field for its competitors. The new details come after Mehta ruled in September that Google would not have to sell off Chrome, as the DOJ proposed at the end of 2024. This all follows the ruling last fall that Google illegally maintained an internet search monopoly through actions including paying companies such as Apple to make its search engine the default on their devices and making exclusive deals around the distribution of services such as Search, Chrome and Gemini.

Senate Republicans are winning the AI regulation moratorium battle as debate continues over federal framework versus states' rights in artificial intelligence policy.

'U.S. sanctions equate us with drug traffickers,' ICC deputy prosecutor says The Hague - The deputy prosecutor of the International Criminal Court on Friday lashed out at U.S. sanctions, arguing they effectively put top court officials on a par with terrorists and drug traffickers. In a wide-ranging interview, Mame Mandiaye Niang also said it would be conceivable to hold an in-absentia hearing against high-level ICC targets such as Israeli Prime Minister Benjamin Netanyahu. Sixty-five-year-old Niang, along with top ICC judges, is subject to sanctions from the administration of U.S. President Donald Trump, in retaliation at the court's arrest warrants for Netanyahu over Israel's campaign in Gaza. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Then its sales blew up. But fails to meet the lofty goals of its own ideas. Shortly before the December 2 release of horror game, developer Santa Ragione shared some news: the game would not be available on Valve's mega platform, Steam . Valve had already banned an early, incomplete version of the game two years ago and offered, according to Santa Ragione, little clarification about why at the time. Then, hours before the game's release, the Epic Games Store banned as well.

New York Times newspaper office building is seen in Manhattan on 26 October 2022. New York Times newspaper office building is seen in Manhattan on 26 October 2022. The New York Times sued an embattled artificial intelligence startup on Friday, accusing the firm of illegally copying millions of articles. The newspaper alleged Perplexity AI had distributed and displayed journalists' work without permission en masse. The Times said that Perplexity AI was also violating its trademarks under the Lanham Act, claiming the startup's generative AI products create fabricated content, or "hallucinations", and falsely attribute them to the newspaper by displaying them alongside its registered trademarks.