With the emergence of widely available powerful LLMs, disinformation generated by large Language Models (LLMs) has become a major concern. Historically, LLM detectors have been touted as a solution, but their effectiveness in the real world is still to be proven. In this paper, we focus on an important setting in information operations -- short news-like posts generated by moderately sophisticated attackers. We demonstrate that existing LLM detectors, whether zero-shot or purpose-trained, are not ready for real-world use in that setting. All tested zero-shot detectors perform inconsistently with prior benchmarks and are highly vulnerable to sampling temperature increase, a trivial attack absent from recent benchmarks. A purpose-trained detector generalizing across LLMs and unseen attacks can be developed, but it fails to generalize to new human-written texts. We argue that the former indicates domain-specific benchmarking is needed, while the latter suggests a trade-off between the adversarial evasion resilience and overfitting to the reference human text, with both needing evaluation in benchmarks and currently absent. We believe this suggests a re-consideration of current LLM detector benchmarking approaches and provides a dynamically extensible benchmark to allow it (https://github.com/Reliable-Information-Lab-HEVS/dynamic_llm_detector_benchmark).

This paper investigates the RWKV model's efficacy in content moderation through targeted experimentation. We introduce a novel dataset specifically designed for distillation into smaller models, enhancing content moderation practices. This comprehensive dataset encompasses images, videos, sounds, and text data that present societal challenges. Leveraging advanced Large Language Models (LLMs), we generated an extensive set of responses -- 558,958 for text and 83,625 for images -- to train and refine content moderation systems. Our core experimentation involved fine-tuning the RWKV model, capitalizing on its CPU-efficient architecture to address large-scale content moderation tasks. By highlighting the dataset's potential for knowledge distillation, this study not only demonstrates RWKV's capability in improving the accuracy and efficiency of content moderation systems but also paves the way for developing more compact, resource-efficient models in this domain. Datasets and models can be found in HuggingFace: https://huggingface.co/modrwkv

Detecting false information on social media is critical in mitigating its negative societal impacts. To reduce the propagation of false information, automated detection provide scalable, unbiased, and cost-effective methods. However, there are three potential research areas identified which once solved improve detection. First, current AI-based solutions often provide a uni-dimensional analysis on a complex, multi-dimensional issue, with solutions differing based on the features used. Furthermore, these methods do not account for the temporal and dynamic changes observed within the document's life cycle. Second, there has been little research on the detection of coordinated information campaigns and in understanding the intent of the actors and the campaign. Thirdly, there is a lack of consideration of cross-platform analysis, with existing datasets focusing on a single platform, such as X, and detection models designed for specific platform. This work aims to develop methods for effective detection of false information and its propagation. To this end, firstly we aim to propose the creation of an ensemble multi-faceted framework that leverages multiple aspects of false information. Secondly, we propose a method to identify actors and their intent when working in coordination to manipulate a narrative. Thirdly, we aim to analyse the impact of cross-platform interactions on the propagation of false information via the creation of a new dataset.

In case the many books and films from the Twilight universe haven't provided enough fodder for your fandom, there's a new TV project in the works about the love-em-or-hate-em sparkly vampires of the Pacific Northwest. An animated series adaptation of Midnight Sun is currently in development at Netflix. Published in 2020, Midnight Sun is a companion to the original Twilight novel, telling the same events of that book from the perspective of Edward Cullen. Yes, the sick, masochistic lion gets to share his side of the story of how he falls for the stupid lamb known as Bella Swan. Author Stephanie Meyer will be an executive producer for the series, as she has been for most other projects in the Twilight realm.

With a name that sounds like something a parent would slowly mouth to their infant, NaNoWriMo is an annual "challenge" in which many thousands of seemingly well-adjusted people decide to write a novel in a month. "Do I need something special to write a novel?" the nonprofit that puts on this exquisite torture reasonably asks on its website. National Novel Writing Month began in 1999 with 21 participants, and now nearly half a million take part every November. The event is also the name of the organization that gamifies the exercise, hosting participants on its online platform. To "win" NaNoWriMo, you need to produce a minimum of 50,000 words in a month (about the length of The Great Gatsby)--or 1,667 words a day, a number, NaNoWriMo tells us, that "scientists have determined to be the perfect amount to boost your creativity."

Fox News chief political anchor Bret Baier has the latest on the pros and cons of the bombshell developments on'Special Report.' The Vatican flag flies outside the United Nations headquarters on Sept. 25, 2015, in New York City. 'PROPER HUMAN CONTROL': A delegation representing the Holy See urged the United Nations this week to put a moratorium on autonomous weapons designed to kill without human decision-making. 'INSANE': Canva is facing pushback from customers over plans to increase subscription prices by more than 300% in some instances. United Nations Headquarters in New York City is seen flanked by Hamas and Hezbollah fighters.

It took a decade, but we finally have a teaser for the live-action A Minecraft Movie. The first look comes courtesy of a video released by Warner Bros. today that clocks in at just over one minute -- but, hey, we'll take it. The film studio has confirmed its previous target, April 4, 2025, is moving forward with a theater-only release. Yes, once upon a time, it had release dates for May 2019 and March 2022, but the existence of a teaser makes us feel a little more hopeful (gullible?) After a series of directors joined and left the project, A Minecraft Movie is led by filmmaker Jared Hess.

Even after a century beneath the water, the Titanic's bow remains one of the most magnificent and haunting sights in the ocean. However, a new survey of the wreck site has revealed that the railing, made famous by Jack and Rose, has now collapsed into rust. Haunting images snapped by underwater robots through the years show the great ship's bow has gradually eroded. Experts say that its metal construction and frequent human visits mean it is only a matter of time before the Titanic collapses. Dr Rodrigo Pacheco-Ruiz, archaeological data manager for HMS Victory and maritime archaeologist from the University of Southampton, told MailOnline: 'The realistic view is that because she's such a big metal object, she won't be there for very long.' Haunting pictures reveal how the Titanic's iconic bow has decayed in the 37 years between 1987 and 2010 Earlier this week, RMS Titanic Inc, the company which holds the salvage rights for the ship, released new images and footage of the sunken liner.

One of the great universal annoyances of life is that TV explosions and soundtracks are always mega-loud, while dialogue is quieter than a church mouse. This is especially true of modern action movies and TV shows that seem to have been mixed primarily to make our walls shake while remaining mostly indiscernible. So we dive into the settings. We turn on the subtitles. Some of this helps, but never enough.

We propose misogyny detection as an Argumentative Reasoning task and we investigate the capacity of large language models (LLMs) to understand the implicit reasoning used to convey misogyny in both Italian and English. The central aim is to generate the missing reasoning link between a message and the implied meanings encoding the misogyny. Our study uses argumentation theory as a foundation to form a collection of prompts in both zero-shot and few-shot settings. These prompts integrate different techniques, including chain-of-thought reasoning and augmented knowledge. Our findings show that LLMs fall short on reasoning capabilities about misogynistic comments and that they mostly rely on their implicit knowledge derived from internalized common stereotypes about women to generate implied assumptions, rather than on inductive reasoning.