This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.

Aerial footage showed the extend of floods in Spain after a series of storms hit the Iberian Peninsula. Storm Marta hit Spain on Saturday, bringing more rain to the region, as it was still recovering from Storm Leonardo. In Córdoba, drone footage showed flooded olive trees as Spanish farmers warned of the millions of euros worth of damage to crops following the torrential rains and high winds. In the country's southern region of Andalucia, over 11,000 people have been displaced. Nazar Daletskyi's relatives were told he had been killed in 2022, the first year of Russia's full-scale invasion.

Russia has been exploiting Ukraine's harshest winter in years to pummel energy infrastructure across the country. Repeated strikes have crippled the power supply to major Ukrainian cities, leaving millions without heating or light as temperatures hover around -15C (5F) for the third week in a row. Electrical companies carry out round-the-clock repairs - only for their work to be undone at night, when Russian drone and missiles again damage power stations. In Kyiv, people were initially able to keep the cold at bay by using electric heaters or wrapping up warm. But the freezing temperatures have lasted weeks now, with no end in sight.

Kyiv is many miles from the front line, but Ukraine's war with Russia is never far away - with Moscow's missile and drone attacks directed at the city almost every day. On the frozen surface of the mighty River Dnipro, the BBC speaks to men who spend hours fishing to take their minds off the almost four-year-old conflict, which has left homes with no heating after Russian strikes on power stations. Drilling holes in the ice of the river in the heart of the city, these ice-fisherman - many of them veterans with friends and family at the front - hope to catch small fish, and a little respite. Authorities deliberately triggered the avalanche on Mount Elbrus to release a build up of snow. The limited deployment involves Germany, France, Sweden, Norway, Finland, the Netherlands and the UK.

Topological methods have the potential of exploring data clouds without making assumptions on their the structure. Here we propose a hierarchical topological clustering algorithm that can be implemented with any distance choice. The persistence of outliers and clusters of arbitrary shape is inferred from the resulting hierarchy. We demonstrate the potential of the algorithm on selected datasets in which outliers play relevant roles, consisting of images, medical and economic data. These methods can provide meaningful clusters in situations in which other techniques fail to do so.

As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left--right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy--authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increased favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: https://github.com/irenestrauss/Democratic-Authoritarian-Bias-LLMs.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency

Today's large language models (LLMs) are capable of supporting multilingual scenarios, allowing users to interact with LLMs in their native languages. When LLMs respond to subjective questions posed by users, they are expected to align with the views of specific demographic groups or historical periods, shaped by the language in which the user interacts with the model. Existing studies mainly focus on researching the opinions represented by LLMs among demographic groups in the United States or a few countries, lacking worldwide country samples and studies on human opinions in different historical periods, as well as lacking discussion on using language to steer LLMs. Moreover, they also overlook the potential influence of prompt language on the alignment of LLMs' opinions. In this study, our goal is to fill these gaps. To this end, we create an evaluation framework based on the World Values Survey (WVS) to systematically assess the alignment of LLMs with human opinions across different countries, languages, and historical periods around the world. We find that LLMs appropriately or over-align the opinions with only a few countries while under-aligning the opinions with most countries. Furthermore, changing the language of the prompt to match the language used in the questionnaire can effectively steer LLMs to align with the opinions of the corresponding country more effectively than existing steering methods. At the same time, LLMs are more aligned with the opinions of the contemporary population. To our knowledge, our study is the first comprehensive investigation of the topic of opinion alignment in LLMs across global, language, and temporal dimensions. Our code and data are publicly available at https://github.com/ku-nlp/global-opinion-alignment and https://github.com/nlply/global-opinion-alignment.

Current studies perceive LLMs' knowledge boundary

Since the inception of our planet, the meteorological environment, as reflected through spatio-temporal data, has always been a fundamental factor influencing human life, socio-economic progress, and ecological conservation.