For the second time in the last month, Microsoft employees disrupted high-level executives speaking at an event celebrating the company's 50th anniversary on 4 April, in protest against the company's role in Israel's ongoing siege on Gaza. The two were fired within days. The Microsoft president Brad Smith and the former CEO Steve Ballmer were shouted down at Seattle's Great Hall on 20 March by a current and former employee. The April event was preceded by a rally outside that also included current and former employees of the tech giant. Protesters projected a sign onto the hall's wall saying, "Microsoft powers genocide" – a reference to Israel's extensive use of the company's AI and cloud computing services since 7 October 2023, as "the IDF's insatiable demand for bombs was matched by its need for greater access to cloud computing services," the Guardian reported.

Despite recent advances in Large Language Models (LLMs) for code generation, the quality of LLM-generated code still faces significant challenges. One significant issue is code repetition, which refers to the model's tendency to generate structurally redundant code, resulting in inefficiencies and reduced readability. To address this, we conduct the first empirical study to investigate the prevalence and nature of repetition across 19 state-of-the-art code LLMs using three widely-used benchmarks. Our study includes both quantitative and qualitative analyses, revealing that repetition is pervasive and manifests at various granularities and extents, including character, statement, and block levels. We further summarize a taxonomy of 20 repetition patterns. Building on our findings, we propose DeRep, a rule-based technique designed to detect and mitigate repetition in generated code. We evaluate DeRep using both open-source benchmarks and in an industrial setting. Our results demonstrate that DeRep significantly outperforms baselines in reducing repetition (with an average improvements of 91.3%, 93.5%, and 79.9% in rep-3, rep-line, and sim-line metrics) and enhancing code quality (with a Pass@1 increase of 208.3% over greedy search). Furthermore, integrating DeRep improves the performance of existing repetition mitigation methods, with Pass@1 improvements ranging from 53.7% to 215.7%.

Eradicating poverty is the first goal in the United Nations Sustainable Development Goals. However, aporophobia -- the societal bias against people living in poverty -- constitutes a major obstacle to designing, approving and implementing poverty-mitigation policies. This work presents an initial step towards operationalizing the concept of aporophobia to identify and track harmful beliefs and discriminative actions against poor people on social media. In close collaboration with non-profits and governmental organizations, we conduct data collection and exploration. Then we manually annotate a corpus of English tweets from five world regions for the presence of (1) direct expressions of aporophobia, and (2) statements referring to or criticizing aporophobic views or actions of others, to comprehensively characterize the social media discourse related to bias and discrimination against the poor. Based on the annotated data, we devise a taxonomy of categories of aporophobic attitudes and actions expressed through speech on social media. Finally, we train several classifiers and identify the main challenges for automatic detection of aporophobia in social networks. This work paves the way towards identifying, tracking, and mitigating aporophobic views on social media at scale.

We know that language models (LMs) form biases and stereotypes of minorities, leading to unfair treatments of members of these groups, thanks to research mainly in the US and the broader English-speaking world. As the negative behavior of these models has severe consequences for society and individuals, industry and academia are actively developing methods to reduce the bias in LMs. However, there are many under-represented groups and languages that have been overlooked so far. This includes marginalized groups that are specific to individual countries and regions in the English speaking and Western world, but crucially also almost all marginalized groups in the rest of the world. The UN estimates, that between 600 million to 1.2 billion people worldwide are members of marginalized groups and in need for special protection. If we want to develop inclusive LMs that work for everyone, we have to broaden our understanding to include overlooked marginalized groups and low-resource languages and dialects. In this work, we contribute to this effort with the first study investigating offensive stereotyping bias in 23 LMs for 270 marginalized groups from Egypt, the remaining 21 Arab countries, Germany, the UK, and the US. Additionally, we investigate the impact of low-resource languages and dialects on the study of bias in LMs, demonstrating the limitations of current bias metrics, as we measure significantly higher bias when using the Egyptian Arabic dialect versus Modern Standard Arabic. Our results show, LMs indeed show higher bias against many marginalized groups in comparison to dominant groups. However, this is not the case for Arabic LMs, where the bias is high against both marginalized and dominant groups in relation to religion and ethnicity. Our results also show higher intersectional bias against Non-binary, LGBTQIA+ and Black women.

Ukraine has reported dozens of civilian deaths from Russian attacks over the past week, including three killed in a late-night assault on Wednesday in the southeastern city of Dnipro. A child was among the victims of the drone attack, which came hours before high-stakes meetings in Paris due to take place later on Thursday, during which United States Secretary of State Marco Rubio and special envoy to the Middle East Steve Witkoff are to meet French President Emmanuel Macron and other European officials to discuss the conflict. Ukraine's defence and foreign ministers, as well as President Volodymyr Zelenskyy's chief of staff, are also in the French capital for talks with US and European Union delegations, though Kyiv's delegation has not specified who it plans to meet. But as Moscow's self-imposed 30-day ceasefire on energy infrastructure approached its close, talks to achieve a broader ceasefire so far have showed little sign of progress. Russia has stuck to its hardline positions while accusing Ukraine of violating the energy ceasefire, to which Kyiv never agreed.

Reward models (RMs) are essential for aligning Large Language Models (LLMs) with human preferences. However, they often struggle with capturing complex human preferences and generalizing to unseen data. To address these challenges, we introduce Energy-Based Reward Model (EBRM), a lightweight post-hoc refinement framework that enhances RM robustness and generalization. EBRM models the reward distribution explicitly, capturing uncertainty in human preferences and mitigating the impact of noisy or misaligned annotations. It achieves this through conflict-aware data filtering, label-noise-aware contrastive training, and hybrid initialization. Notably, EBRM enhances RMs without retraining, making it computationally efficient and adaptable across different models and tasks. Empirical evaluations on RM benchmarks demonstrate significant improvements in both robustness and generalization, achieving up to a 5.97% improvement in safety-critical alignment tasks compared to standard RMs. Furthermore, reinforcement learning experiments confirm that our refined rewards enhance alignment quality, effectively delaying reward hacking. These results demonstrate our approach as a scalable and effective enhancement for existing RMs and alignment pipelines. The code is available at EBRM.

We study the approximation gap between the dynamics of a polynomial-width neural network and its infinite-width counterpart, both trained using projected gradient descent in the mean-field scaling regime. We demonstrate how to tightly bound this approximation gap through a differential equation governed by the mean-field dynamics. A key factor influencing the growth of this ODE is the local Hessian of each particle, defined as the derivative of the particle's velocity in the mean-field dynamics with respect to its position. We apply our results to the canonical feature learning problem of estimating a well-specified single-index model; we permit the information exponent to be arbitrarily large, leading to convergence times that grow polynomially in the ambient dimension $d$. We show that, due to a certain ``self-concordance'' property in these problems -- where the local Hessian of a particle is bounded by a constant times the particle's velocity -- polynomially many neurons are sufficient to closely approximate the mean-field dynamics throughout training.

Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract the distinct aspects called archetypes in observations with each observational record approximated as a mixture (i.e., convex combination) of these archetypes. AA thereby provides straightforward, interpretable, and explainable representations for feature extraction and dimensionality reduction, facilitating the understanding of the structure of high-dimensional data with wide applications throughout the sciences. However, AA also faces challenges, particularly as the associated optimization problem is non-convex. This survey provides researchers and data mining practitioners an overview of methodologies and opportunities that AA has to offer surveying the many applications of AA across disparate fields of science, as well as best practices for modeling data using AA and limitations. The survey concludes by explaining important future research directions concerning AA.

Large language models (LLMs) are transforming global decision-making and societal systems by processing diverse data at unprecedented scales. However, their potential to homogenize human values poses critical risks, similar to biodiversity loss undermining ecological resilience. Rooted in the ancient Greek concept of ethos, meaning both individual character and the shared moral fabric of communities, EthosGPT draws on a tradition that spans from Aristotle's virtue ethics to Adam Smith's moral sentiments as the ethical foundation of economic cooperation. These traditions underscore the vital role of value diversity in fostering social trust, institutional legitimacy, and long-term prosperity. EthosGPT addresses the challenge of value homogenization by introducing an open-source framework for mapping and evaluating LLMs within a global scale of human values. Using international survey data on cultural indices, prompt-based assessments, and comparative statistical analyses, EthosGPT reveals both the adaptability and biases of LLMs across regions and cultures. It offers actionable insights for developing inclusive LLMs, such as diversifying training data and preserving endangered cultural heritage to ensure representation in AI systems. These contributions align with the United Nations Sustainable Development Goals (SDGs), especially SDG 10 (Reduced Inequalities), SDG 11.4 (Cultural Heritage Preservation), and SDG 16 (Peace, Justice and Strong Institutions). Through interdisciplinary collaboration, EthosGPT promotes AI systems that are both technically robust and ethically inclusive, advancing value plurality as a cornerstone for sustainable and equitable futures.

This article was originally featured on The Conversation. Languages are windows into the worlds of the people who speak them – reflecting what they value and experience daily. So perhaps it's no surprise different languages highlight different areas of vocabulary. Scholars have noted that Mongolian has many horse-related words, that Maori has many words for ferns, and Japanese has many words related to taste. Some links are unsurprising, such as German having many words related to beer, or Fijian having many words for fish.