As large language models (LLMs) become integrated into everyday applications, ensuring their robustness and security is increasingly critical. In particular, LLMs can be manipulated into unsafe behaviour by prompts known as jailbreaks. The variety of jailbreak styles is growing, necessitating the use of external defences known as guardrails. While many jailbreak defences have been proposed, not all defences are able to handle new out-of-distribution attacks due to the narrow segment of jailbreaks used to align them. Moreover, the lack of systematisation around defences has created significant gaps in their practical application. In this work, we perform systematic benchmarking across 15 different defences, considering a broad swathe of malicious and benign datasets. We find that there is significant performance variation depending on the style of jailbreak a defence is subject to. Additionally, we show that based on current datasets available for evaluation, simple baselines can display competitive out-of-distribution performance compared to many state-of-the-art defences. Code is available at https://github.com/IBM/Adversarial-Prompt-Evaluation.

Robust automatic fact-checking systems have the potential to combat online misinformation at scale. However, most existing research primarily focuses on English. In this paper, we introduce MultiSynFact, the first large-scale multilingual fact-checking dataset containing 2.2M claim-source pairs designed to support Spanish, German, English, and other low-resource languages. Our dataset generation pipeline leverages Large Language Models (LLMs), integrating external knowledge from Wikipedia and incorporating rigorous claim validation steps to ensure data quality. We evaluate the effectiveness of MultiSynFact across multiple models and experimental settings. Additionally, we open-source a user-friendly framework to facilitate further research in multilingual fact-checking and dataset generation.

Variation in language is ubiquitous and often systematically linked to regional, social, and contextual factors. Tokenizers split texts into smaller units and might behave differently for less common linguistic forms. This might affect downstream LLM performance differently on two types of tasks: Tasks where the model should be robust to language variation (e.g., for semantic tasks like NLI, labels do not depend on whether a text uses British or American spelling) and tasks where the model should be sensitive to language variation (e.g., for form-based tasks like authorship verification, labels depend on whether a text uses British or American spelling). We pre-train BERT base models for the popular Byte-Pair Encoding algorithm to investigate how key algorithmic design choices impact downstream models' performances: fitting corpus, pre-tokenizer and vocabulary size. We find that the best tokenizer varies on the two task types -- with the pre-tokenizer having the biggest impact on performance. Further, we introduce a new approach to estimate tokenizer impact on downstream LLM performance, showing significant improvement over techniques like R\'enyi efficiency. We encourage more work on language variation and its relation to tokenizers and thus LLM performance.

Music composition has long been recognized as a significant art form. However, existing digital audio workstations and music production software often present high entry barriers for users lacking formal musical training. To address this, we introduce ComposeOn, a music theory-based tool designed for users with limited musical knowledge. ComposeOn enables users to easily extend their melodic ideas into complete compositions and offers simple editing features. By integrating music theory, it explains music creation at beginner, intermediate, and advanced levels. Our user study (N=10) compared ComposeOn with the baseline method, Suno AI, demonstrating that ComposeOn provides a more accessible and enjoyable composing and learning experience for individuals with limited musical skills. ComposeOn bridges the gap between theory and practice, offering an innovative solution as both a composition aid and music education platform. The study also explores the differences between theory-based music creation and generative music, highlighting the former's advantages in personal expression and learning.

Many modern methods for prediction leverage nearest neighbor search to find past training examples most similar to a test example, an idea that dates back in text to at least the 11th century and has stood the test of time. This monograph aims to explain the success of these methods, both in theory, for which we cover foundational nonasymptotic statistical guarantees on nearest-neighbor-based regression and classification, and in practice, for which we gather prominent methods for approximate nearest neighbor search that have been essential to scaling prediction systems reliant on nearest neighbor analysis to handle massive datasets. Furthermore, we discuss connections to learning distances for use with nearest neighbor methods, including how random decision trees and ensemble methods learn nearest neighbor structure, as well as recent developments in crowdsourcing and graphons. In terms of theory, our focus is on nonasymptotic statistical guarantees, which we state in the form of how many training data and what algorithm parameters ensure that a nearest neighbor prediction method achieves a user-specified error tolerance. We begin with the most general of such results for nearest neighbor and related kernel regression and classification in general metric spaces. In such settings in which we assume very little structure, what enables successful prediction is smoothness in the function being estimated for regression, and a low probability of landing near the decision boundary for classification. In practice, these conditions could be difficult to verify for a real dataset. We then cover recent guarantees on nearest neighbor prediction in the three case studies of time series forecasting, recommending products to people over time, and delineating human organs in medical images by looking at image patches. In these case studies, clustering structure enables successful prediction.

After months of anticipation, Apple finally unveiled its latest smartphone to the world last night. The iPhone 16e is Apple's latest'budget' smartphone, with prices starting at just 599/ 599. The new device runs Apple Intelligence features, including a ChatGPT integration with smart assistant Siri. It also includes a 6.1-inch display, a two-in-one camera system, an'extraordinary' battery life, and the return of the'notch' at the top of the display. While the focus has been on the new device, several eagle-eyed Apple fans have noticed that three popular devices have been quietly discontinued.

Despite all beliefs to the contrary, the billionaires who have been seen in President Donald Trump's orbit since he won the presidency for a second time last November are not mere sycophants to his regime. Former Washington Post political cartoonist Ann Telnaes should know. Last month, Telnaes quit her job after her editor refused to publish what turned out to be her last cartoon for the newspaper. In it, Telnaes drew Amazon and Washington Post owner Jeff Bezos, Los Angeles Times owner Patrick Soon-Shiong, OpenAI billionaire Sam Altman, Meta's Mark Zuckerberg, and Mickey Mouse (representing media giant Disney/American Broadcasting Company) either kneeling or bowing face down in front of a statue of the president. In explaining her decision to resign from the Post, Telnaes wrote, "Owners of such press organizations are responsible for safeguarding that free press – and trying to get in the good graces of an autocrat-in-waiting will only result in undermining that free press."

It's long been possible to say "Hey Google" to your Google smart speaker to request a playlist from, say, YouTube Music, Spotify, Pandora, and even Apple Music. But can you spot the major music service that's missing? Until now, Amazon Music had been conspicuously absent from the list of music streamers that Google Assistant could easily control on your Google Nest smart speaker or display. Recently, though, Google has begun changing its tune in regard to Amazon Music support on its Nest devices. As spotted by 9to5Google, Amazon Music can finally be set as a default music service on your Google smart speakers.

Products featured in this Shopping Finder article are selected by our shopping writers. If you make a purchase using links on this page, Dailymail.co.uk will earn an affiliate commission. After months of anticipation, Apple finally unveiled its latest smartphone to the world last night - the iPhone 16e. The device runs Apple Intelligence features, including a ChatGPT integration with smart assistant Siri. It also includes a 6.1-inch display, a two-in-one camera system, an'extraordinary' battery life, and the return of the'notch' at the top of the display.

This book provides a broad overview of Natural Language Generation (NLG), including technology, user requirements, evaluation, and real-world applications. The focus is on concepts and insights which hopefully will remain relevant for many years, not on the latest LLM innovations. It draws on decades of work by the author and others on NLG. The book has the following chapters: Introduction to NLG; Rule-Based NLG; Machine Learning and Neural NLG; Requirements; Evaluation; Safety, Maintenance, and Testing; and Applications. All chapters include examples and anecdotes from the author's personal experiences, and end with a Further Reading section. The book should be especially useful to people working on applied NLG, including NLG researchers, people in other fields who want to use NLG, and commercial developers. It will not however be useful to people who want to understand the latest LLM technology. There is a companion site with more information at https://ehudreiter.com/book/