Evaluating natural language generation (NLG) systems remains a core challenge of natural language processing (NLP), further complicated by the rise of large language models (LLMs) that aim to be general-purpose. Recently, large language models as judges (LLJs) have emerged as a promising alternative to traditional metrics, but their validity remains underexplored. This position paper argues that the current enthusiasm around LLJs may be premature, as their adoption has outpaced rigorous scrutiny of their reliability and validity as evaluators. Drawing on measurement theory from the social sciences, we identify and critically assess four core assumptions underlying the use of LLJs: their ability to act as proxies for human judgment, their capabilities as evaluators, their scalability, and their cost-effectiveness. We examine how each of these assumptions may be challenged by the inherent limitations of LLMs, LLJs, or current practices in NLG evaluation. To ground our analysis, we explore three applications of LLJs: text summarization, data annotation, and safety alignment. Finally, we highlight the need for more responsible evaluation practices in LLJs evaluation, to ensure that their growing role in the field supports, rather than undermines, progress in NLG.

Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstract and complex phenomena such as'safety' and'robustness' requires strong construct validity, that is, having measures that represent what matters to the phenomenon. With a team of 29 expert reviewers, we conduct a systematic review of 445 LLM benchmarks from leading conferences in natural language processing and machine learning. Across the reviewed articles, we find patterns related to the measured phenomena, tasks, and scoring metrics which undermine the validity of the resulting claims. To address these shortcomings, we provide eight key recommendations and detailed actionable guidance to researchers and practitioners in developing LLM benchmarks.

Venues hosting the 2026 World Cup must meet high standards to obtain environmental certifications, but FIFA also requires that they use natural grass, which is water-intensive to maintain. Estadio Banorte, formerly called Azteca stadium, in Mexico City. Because of their scale, soccer stadiums require a fair amount of energy and water. In that time, they also generate large volumes of waste, mainly plastics and food trash. For the 2026 World Cup, the first to be held in three countries in 16 different stadiums, FIFA maintained the requirement that the venues must have LEED environmental certifications, which measure performance in water, energy, and waste management.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Flag football will step firmly onto the global stage at the 2028 Olympics in Los Angeles, and Diana Flores is one of the key figures who helped it get there. The Mexico City native began playing at age 8 and made her country's national team by 16.

I own 20 axolotls - people need to know they're not easy to look after When Emma Honeyfield's daughter Amber asked for an axolotl for her birthday, Emma never imagined it would lead to a collection of 20. The 37-year-old bought her daughter's first axolotl, Stitch, in September and has since fallen in love with their calming nature. Emma said Amber, eight, had always been difficult to buy for, so when she asked for one for her birthday, she couldn't say no. And the family, from Tredegar, Blaenau Gwent, are far from alone in seeking out the amphibians, which are critically endangered and only found in lakes and wetlands in southern Mexico City . The animal's cute, smiling face and appearance in the hugely popular Minecraft and Roblox games has seen an increase in the number of people keeping them as pets.

In-context learning enables transformers to adapt to new tasks from a few examples at inference time, while grokking highlights that this generalization can emerge abruptly only after prolonged training. We study task generalization and grokking in in-context learning using a Bayesian perspective, asking what enables the delayed transition from memorization to generalization. Concretely, we consider modular arithmetic tasks in which a transformer must infer a latent linear function solely from in-context examples and analyze how predictive uncertainty evolves during training. We combine approximate Bayesian techniques to estimate the posterior distribution and we study how uncertainty behaves across training and under changes in task diversity, context length, and context noise. We find that epistemic uncertainty collapses sharply when the model groks, making uncertainty a practical label-free diagnostic of generalization in transformers. Additionally, we provide theoretical support with a simplified Bayesian linear model, showing that asymptotically both delayed generalization and uncertainty peaks arise from the same underlying spectral mechanism, which links grokking time to uncertainty dynamics.

The launch of "Xoli" adds to the technological efforts promoted by the federal government to turn the 2026 World Cup into an engine of development for the entire country. Xoli, the new chatbot, is named after the axolotl, a salamander with external gills. The Government of Mexico City has launched Xoli, a chatbot that will provide information on services, tourism, and cultural offerings. The platform was designed to meet the demand of the millions of visitors expected to arrive during the 2026 FIFA World Cup . However, the authorities assure that the tool will remain active once the sporting event is over, with the aim of promoting economic activities and facilitating access to public services in the capital.

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A new framework is proposed in this paper to address these issues.