The first Workshop on Language Models for Low-Resource Languages (LoResLM 2025) was held in conjunction with the 31st International Conference on Computational Linguistics (COLING 2025) in Abu Dhabi, United Arab Emirates. This workshop mainly aimed to provide a forum for researchers to share and discuss their ongoing work on language models (LMs) focusing on low-resource languages, following the recent advancements in neural language models and their linguistic biases towards high-resource languages. LoResLM 2025 attracted notable interest from the natural language processing (NLP) community, resulting in 35 accepted papers from 52 submissions. These contributions cover a broad range of low-resource languages from eight language families and 13 diverse research areas, paving the way for future possibilities and promoting linguistic inclusivity in NLP.

At least one person was injured and several homes damaged in a Russian drone attack on Ukraine's Kyiv region, Governor Ruslan Kravchenko said. Ukraine's Air Force said it shot down 53 of the 56 Russian drones that targeted the country's central, northern and southern regions. Air defence units destroyed nearly 20 drones that were heading towards Kyiv itself, the military said. Ukrainian President Volodymyr Zelenskyy, speaking in his nightly video address, said there had been 100 battles over the past 24 hours on the eastern front with the heaviest fighting in the Pokrovsk and Kurakhove sectors. Russia ordered the evacuation of settlements close to the Ukrainian border in the Kursk region and said it had retaken two villages – Uspenovka and Borki – Ukraine captured last month in a surprise cross-border incursion.

This paper embarks on an exploration into the Large Language Model (LLM) datasets, which play a crucial role in the remarkable advancements of LLMs. The datasets serve as the foundational infrastructure analogous to a root system that sustains and nurtures the development of LLMs. Consequently, examination of these datasets emerges as a critical topic in research. In order to address the current lack of a comprehensive overview and thorough analysis of LLM datasets, and to gain insights into their current status and future trends, this survey consolidates and categorizes the fundamental aspects of LLM datasets from five perspectives: (1) Pre-training Corpora; (2) Instruction Fine-tuning Datasets; (3) Preference Datasets; (4) Evaluation Datasets; (5) Traditional Natural Language Processing (NLP) Datasets. The survey sheds light on the prevailing challenges and points out potential avenues for future investigation. Additionally, a comprehensive review of the existing available dataset resources is also provided, including statistics from 444 datasets, covering 8 language categories and spanning 32 domains. Information from 20 dimensions is incorporated into the dataset statistics. The total data size surveyed surpasses 774.5 TB for pre-training corpora and 700M instances for other datasets. We aim to present the entire landscape of LLM text datasets, serving as a comprehensive reference for researchers in this field and contributing to future studies. Related resources are available at: https://github.com/lmmlzn/Awesome-LLMs-Datasets.

Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in memes. In this paper, we propose a novel model, cluster-based deep ensemble learning (CDEL), for emotion classification in memes. CDEL is a hybrid model that leverages the benefits of a deep learning model in combination with a clustering algorithm, which enhances the model with additional information after clustering memes with similar facial features. We evaluate the performance of CDEL on a benchmark dataset for emotion classification, proving its effectiveness by outperforming a wide range of baseline models and achieving state-of-the-art performance. Further evaluation through ablated models demonstrates the effectiveness of the different components of CDEL.

If we're looking for that one terminator to show up at our door, we're maybe looking in the wrong place,

NAIROBI (Thomson Reuters Foundation) - Countries are rapidly developing "killer robots" - machines with artificial intelligence (AI) that independently kill - but are moving at a snail's pace on agreeing global rules over their use in future wars, warn technology and human rights experts. From drones and missiles to tanks and submarines, semi-autonomous weapons systems have been used for decades to eliminate targets in modern day warfare - but they all have human supervision. Nations such as the United States, Russia and Israel are now investing in developing lethal autonomous weapons systems (LAWS) which can identify, target, and kill a person all on their own - but to date there are no international laws governing their use. "Some kind of human control is necessary ... Only humans can make context-specific judgements of distinction, proportionality and precautions in combat," said Peter Maurer, President of the International Committee of the Red Cross (ICRC).

From ship-hunting Tomahawk missiles and sub-spying drone ships to semi-autonomous UAV swarms and situationally-aware reconnaissance robots, the Pentagon has long sought to protect its human forces with the use of robotic weapons. But as these systems gain ever-greater degrees of intelligence and independence, their increasing autonomy has some critics worried that humans are ceding too much power to devices whose decision-making processes we don't fully understand (and which we may not be entirely able to control). What constitutes an Autonomous Weapon System (AWS) depends on who you ask, as these systems exhibit varying degrees of independence. Sense and React to Military Objects (SARMO) weapons like the Phalanx and C-RAM are able to react to incoming artillery and missile threats, targeting and engaging them without human oversight. However these aren't fully-autonomous, per se -- they simply perform a set automated task.

A very, very small quadcopter, one inch in diameter can carry a one- or two-gram shaped charge. You can order them from a drone manufacturer in China. You can program the code to say: "Here are thousands of photographs of the kinds of things I want to target." A one-gram shaped charge can punch a hole in nine millimeters of steel, so presumably you can also punch a hole in someone's head. You can fit about three million of those in a semi-tractor-trailer. You can drive up I-95 with three trucks and have 10 million weapons attacking New York City. They don't have to be very effective, only 5 or 10% of them have to find the target. There will be manufacturers producing millions of these weapons that people will be able to buy just like you can buy guns now, except millions of guns don't matter unless you have a million soldiers. You need only three guys to write the program and launch them. So you can just imagine that in many parts of the world humans will be hunted. They will be cowering underground in shelters and devising techniques so that they don't get detected. This is the ever-present cloud of lethal autonomous weapons. Mary Wareham laughs a lot. It usually sounds the same regardless of the circumstance -- like a mirthful giggle the blonde New Zealander can't suppress -- but it bubbles up at the most varied moments. Wareham laughs when things are funny, she laughs when things are awkward, she laughs when she disagrees with you. And she laughs when things are truly unpleasant, like when you're talking to her about how humanity might soon be annihilated by killer robots and the world is doing nothing to stop it. One afternoon this spring at the United Nations in Geneva, I sat behind Wareham in a large wood-paneled, beige-carpeted assembly room that hosted the Convention on Certain Conventional Weapons (CCW), a group of 121 countries that have signed the agreement to restrict weapons that "are considered to cause unnecessary or unjustifiable suffering to combatants or to affect civilians indiscriminately"-- in other words, weapons humanity deems too cruel to use in war. The UN moves at a glacial pace, but the CCW is even worse.