In 2012, the United Nations introduced 17 Sustainable Development Goals (SDGs) aimed at creating a more sustainable and improved future by 2030. However, tracking progress toward these goals is difficult because of the extensive scale and complexity of the data involved. Text classification models have become vital tools in this area, automating the analysis of vast amounts of text from a variety of sources. Additionally, large language models (LLMs) have recently proven indispensable for many natural language processing tasks, including text classification, thanks to their ability to recognize complex linguistic patterns and semantics. This study analyzes various proprietary and open-source LLMs for a single-label, multi-class text classification task focused on the SDGs. Then, it also evaluates the effectiveness of task adaptation techniques (i.e., in-context learning approaches), namely Zero-Shot and Few-Shot Learning, as well as Fine-Tuning within this domain. The results reveal that smaller models, when optimized through prompt engineering, can perform on par with larger models like OpenAI's GPT (Generative Pre-trained Transformer).

Kyiv also said that Russian forces launched two missile strikes and 72 air strikes, and used 1,024 kamikaze drones, along with 4,200 artillery attacks that targeted Ukrainian positions and settlements, AA reports. In Ukraine's Kharkiv region, Ukrainian forces said they prevented Russian advances towards Mala Shapkivka and Topoli, while Moscow's troops launched 16 attacks in Ukraine's Kupiansk region, with Kyiv's forces claiming to have repelled 14, as battles continue, Anadolu reports. Russia said oil flows through the Caspian Pipeline Consortium, a major route for supplying Kazakhstan and exporting to the global market, have been reduced by 30 to 40 percent after a Ukrainian drone attack on a pumping station. The Caspian pipeline, which ships more than 1 percent of daily global oil supplies, stretches over 1,500km (939 miles) and carries crude oil from Kazakhstan's Tengiz oilfield on Russia's northeastern shores of the Caspian Sea as well as from Russian producers. Freedom in Russia and the end of Russian President Vladimir Putin's government depends on Ukraine winning the war, former chess world champion and Kremlin critic Garry Kasparov said.

Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains. To address this, we introduce and open-source a large-scale (10,600 samples) instruction-following (IFT) dataset, covering key institutional and cultural knowledge relevant to Kazakhstan. Our dataset enhances LLMs' understanding of procedural, legal, and structural governance topics. We employ LLM-assisted data generation, comparing open-weight and closed-weight models for dataset construction, and select GPT-4o as the backbone. Each entity of our dataset undergoes full manual verification to ensure high quality. We also show that fine-tuning Qwen, Falcon, and Gemma on our dataset leads to consistent performance improvements in both multiple-choice and generative tasks, demonstrating the potential of LLM-assisted instruction tuning for low-resource languages.

Fox News correspondent Stephanie Bennett has the latest on the aftermath of the Kazakhstan plane crash on'Special Report.' Crew members and survivors of the Azerbaijan Airlines plane that crashed in Kazakhstan on Christmas Day say they heard at least one loud bang before the aircraft crashed in a ball of fire, heightening speculation that a Russian anti-aircraft missile may have been responsible for the tragedy. The Embraer 190 passenger jet flying from Azerbaijan to Russia crashed near the city of Aktau in Kazakhstan after diverting from an area of southern Russia where Moscow has repeatedly used air defense systems against Ukrainian attack drones. At least 38 people were killed while 29 survived. Subhonkul Rakhimov, one of the passengers aboard Flight J2-8243, told Reuters from the hospital that he had begun to recite prayers and prepare for the end after hearing a bang.

A central question in knowledge representation is the following: given some knowledge representation formalism, is it possible, and if so how, to simplify parts of a knowledge base without affecting its meaning, even in the light of additional information? The term strong equivalence was coined in the literature, i.e. strongly equivalent knowledge bases can be locally replaced by each other in a bigger theory without changing the semantics of the latter. In contrast to classical (monotone) logics where standard and strong equivalence coincide, it is possible to find ordinary but not strongly equivalent objects for any nonmonotonic formalism available in the literature. This paper addresses these questions in the context of abstract argumentation theory. Much effort has been spent to characterize several argumentation tailored equivalence notions w.r.t. extension-based semantics. In recent times labelling-based semantics have received increasing attention, for example in connection with algorithms computing extensions, proof procedures, dialogue games, dynamics in argumentation as well as belief revision in general. Of course, equivalence notions allowing for replacements are of high interest for the mentioned topics. In this paper we provide kernel-based characterization theorems for semantics based on complete labellings as well as admissible labellings w.r.t. eight different equivalence notions including the aforementioned most prominent one, namely strong equivalence.