Intent classification models have made a significant progress in recent years. However, previous studies primarily focus on high-resource language datasets, which results in a gap for low-resource languages and for regions with high rates of illiteracy, where languages are more spoken than read or written. This is the case in Senegal, for example, where Wolof is spoken by around 90\% of the population, while the national illiteracy rate remains at of 42\%. Wolof is actually spoken by more than 10 million people in West African region. To address these limitations, we introduce the Wolof Banking Speech Intent Classification Dataset (WolBanking77), for academic research in intent classification. WolBanking77 currently contains 9,791 text sentences in the banking domain and more than 4 hours of spoken sentences. Experiments on various baselines are conducted in this work, including text and voice state-of-the-art models. The results are very promising on this current dataset. In addition, this paper presents an in-depth examination of the dataset's contents. We report baseline F1-scores and word error rates metrics respectively on NLP and ASR models trained on WolBanking77 dataset and also comparisons between models. Dataset and code available at: https://github.com/abdoukarim/wolbanking77.

Despite efforts to mitigate the inherent risks and biases of artificial intelligence (AI) algorithms, these algorithms can disproportionately impact culturally marginalized groups. A range of approaches has been proposed to address or reduce these risks, including the development of ethical guidelines and principles for responsible AI, as well as technical solutions that promote algorithmic fairness. Drawing on design justice, expansive learning theory, and recent empirical work on participatory AI, we argue that mitigating these harms requires a fundamental re-architecture of the AI production pipeline. This re-design should center co-production, diversity, equity, inclusion (DEI), and multidisciplinary collaboration. We introduce an augmented AI lifecycle consisting of five interconnected phases: co-framing, co-design, co-implementation, co-deployment, and co-maintenance. The lifecycle is informed by four multidisciplinary workshops and grounded in themes of distributed authority and iterative knowledge exchange. Finally, we relate the proposed lifecycle to several leading ethical frameworks and outline key research questions that remain for scaling participatory governance.

How can large language models (LLMs) serve users with varying preferences that may conflict across cultural, political, or other dimensions? To advance this challenge, this paper establishes four key results. First, we demonstrate, through a large-scale multilingual human study with representative samples from five countries (N=15,000), that humans exhibit significantly more variation in preferences than the responses of 21 state-of-the-art LLMs. Second, we show that existing methods for preference dataset collection are insufficient for learning the diversity of human preferences even along two of the most salient dimensions of variability in global values, due to the underlying homogeneity of candidate responses. Third, we argue that this motivates the need for negatively-correlated sampling when generating candidate sets, and we show that simple prompt-based techniques for doing so significantly enhance the performance of alignment methods in learning heterogeneous preferences. Fourth, based on this novel candidate sampling approach, we collect and open-source Community Alignment, the largest and most representative multilingual and multi-turn preference dataset to date, featuring almost 200,000 comparisons from annotators spanning five countries. We hope that the Community Alignment dataset will be a valuable resource for improving the effectiveness of LLMs for a diverse global population.

Open AI CEO Sam Altman speaks during a summit on June 2, 2025 in San Francisco, California. Open AI CEO Sam Altman speaks during a summit on June 2, 2025 in San Francisco, California. Founded in 2015 as a nonprofit, rather than a for-profit company, it promised to develop AI "in the way that is most likely to benefit humanity." With billions of dollars in investments from Microsoft, Japanese bank SoftBank, and chipmaker Nvidia, however, OpenAI has proposed changing its corporate structure to give investors more control over its technology. Critics of the change include cofounder-turned-competitor, Elon Musk, and nonprofits concerned about OpenAI's adherence to its mission.

Once in a while, LLMs turn evil--and no one quite knows why. The AI company Anthropic has made a rigorous effort to build a large language model with positive human values. The $183 billion company's flagship product is Claude, and much of the time, its engineers say, Claude is a model citizen. Its standard persona is warm and earnest. When users tell Claude to "answer like I'm a fourth grader" or "you have a PhD in archeology," it gamely plays along. It makes threats and then carries them out. And the frustrating part--true of all LLMs--is that no one knows exactly why. Consider a recent stress test that Anthropic's safety engineers ran on Claude. In their fictional scenario, the model was to take on the role of Alex, an AI belonging to the Summit Bridge corporation.

Erotic imagery and curiosity often arise in intimate relationships, especially when there's safety, play, and mutual recognition. It doesn't mean you've done anything "wrong." On the contrary, it shows that your imagination is alive and searching for ways to bridge the gap between closeness and distance, fantasy and reality. You offer me something charged, even a bit embarrassing, and you're watching--will I crumble?

Ed Zitron Gets Paid to Love AI. He's one of the loudest voices of the AI haters--even as he does PR for AI companies. Either way, Ed Zitron has your attention. In his day job, Ed Zitron runs a boutique public relations firm called EZPR. This might surprise anyone who has come to know Zitron through his podcast or his social media or the newsletter in which he writes two-fisted stuff like "Sam Altman is full of shit and "Mark Zuckerberg is a putrid ghoul." Flacks, as a rule, tend not to talk like this. Flacks send prim, throat-clearing emails to media people who do, on rare occasions, talk like this. Flacks want to touch base, hop on the phone, clear up a few things about the allegation that their CEO is a "chunderfuck." And that really is one of the things with guys like Sam Altman and Dario Amodei from Anthropic," Zitron was saying over burgers on a fine Manhattan afternoon in September. "I work with founders all the time. I'm a founder myself, I guess--I don't like the title. But when you are a person that has to make more money than you lose, otherwise you lose your business, and you see these chunderfucks burning 5, 10 billion dollars in a year--and everyone's celebrating them? We were talking about whether any of Zitron's ranting about the AI industry had cost him business on the PR side of the ledger. There was the one client who felt Zitron was being a little mean toward Altman, the CEO of OpenAI and the biggest chunderfuck of all, as far as Zitron is concerned. Founding a company is hard, the client said. "I said, 'I appreciate the comment, but, like, this isn't about you,'" Zitron told me. "His company is burning billions of dollars.

In recent times, there have been two techno-religious awakenings. To be human is to yearn for a Sky Daddy. Something that explains the unexplainable, someone to blame. No wonder, then, that in the ZIRP-fueled 2010s, when a new gospel of creation was being spread, some people started to see technology as a kind of religion. Startup founders and CEOs became messianic figures.

Reinforcement learning (RL) has become a predominant technique to align language models (LMs) with human preferences or promote outputs which are deemed to be desirable by a given reward function. Standard RL approaches optimize average reward, while methods explicitly focused on reducing the probability of undesired outputs typically come at a cost to average-case performance. To improve this tradeoff, we introduce RePULSe, a new training method that augments the standard RL loss with an additional loss that uses learned proposals to guide sampling low-reward outputs, and then reduces those outputs' probability. We run experiments demonstrating that RePULSe produces a better tradeoff of expected reward versus the probability of undesired outputs and is more adversarially robust, compared to standard RL alignment approaches and alternatives.

Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple responses from the base policy for search using a reward model, which can be considered as searching in a discrete response space. However, these methods struggle to explore informative candidates when the base policy is weak or the candidate set is small, resulting in limited effectiveness. In this paper, to address this problem, we propose Simple Energy Adaptation ($\textbf{SEA}$), a simple yet effective algorithm for inference-time alignment. In contrast to expensive search over the discrete space, SEA directly adapts original responses from the base policy toward the optimal one via gradient-based sampling in continuous latent space. Specifically, SEA formulates inference as an iterative optimization procedure on an energy function over actions in the continuous space defined by the optimal policy, enabling simple and effective alignment. For instance, despite its simplicity, SEA outperforms the second-best baseline with a relative improvement of up to $ \textbf{77.51%}$ on AdvBench and $\textbf{16.36%}$ on MATH. Our code is publicly available at https://github.com/yuanyige/sea