As AI systems are used to answer more difficult questions and potentially help create new knowledge, judging the truthfulness of their outputs becomes more difficult and more important. How can we supervise unreliable experts, which have access to the truth but may not accurately report it, to give answers that are systematically true and don't just superficially seem true, when the supervisor can't tell the difference between the two on their own? In this work, we show that debate between two unreliable experts can help a non-expert judge more reliably identify the truth. We collect a dataset of human-written debates on hard reading comprehension questions where the judge has not read the source passage, only ever seeing expert arguments and short quotes selectively revealed by 'expert' debaters who have access to the passage. In our debates, one expert argues for the correct answer, and the other for an incorrect answer. Comparing debate to a baseline we call consultancy, where a single expert argues for only one answer which is correct half of the time, we find that debate performs significantly better, with 84% judge accuracy compared to consultancy's 74%. Debates are also more efficient, being 68% of the length of consultancies. By comparing human to AI debaters, we find evidence that with more skilled (in this case, human) debaters, the performance of debate goes up but the performance of consultancy goes down. Our error analysis also supports this trend, with 46% of errors in human debate attributable to mistakes by the honest debater (which should go away with increased skill); whereas 52% of errors in human consultancy are due to debaters obfuscating the relevant evidence from the judge (which should become worse with increased skill). Overall, these results show that debate is a promising approach for supervising increasingly capable but potentially unreliable AI systems.

The increasing number of RSOs has raised concerns about the risk of collisions and catastrophic incidents for all direct and indirect users of space. To mitigate this issue, it is essential to have a good understanding of the various RSOs in orbit and their behaviour. A well-established taxonomy defining several classes of RSOs is a critical step in achieving this understanding. This taxonomy helps assign objects to specific categories based on their main characteristics, leading to better tracking services. Furthermore, a well-established taxonomy can facilitate research and analysis processes by providing a common language and framework for better understanding the factors that influence RSO behaviour in space. These factors, in turn, help design more efficient and effective strategies for space traffic management. Our work proposes a new taxonomy for RSOs focusing on the low Earth orbit regime to enhance space traffic management. In addition, we present a deep learning-based model that uses an autoencoder architecture to reduce the features representing the characteristics of the RSOs. The autoencoder generates a lower-dimensional space representation that is then explored using techniques such as Uniform Manifold Approximation and Projection to identify fundamental clusters of RSOs based on their unique characteristics. This approach captures the complex and non-linear relationships between the features and the RSOs' classes identified. Our proposed taxonomy and model offer a significant contribution to the ongoing efforts to mitigate the overall risks posed by the increasing number of RSOs in orbit.

Language features are evolving in real-world social media, resulting in the deteriorating performance of text classification in dynamics. To address this challenge, we study temporal adaptation, where models trained on past data are tested in the future. Most prior work focused on continued pretraining or knowledge updating, which may compromise their performance on noisy social media data. To tackle this issue, we reflect feature change via modeling latent topic evolution and propose a novel model, VIBE: Variational Information Bottleneck for Evolutions. Concretely, we first employ two Information Bottleneck (IB) regularizers to distinguish past and future topics. Then, the distinguished topics work as adaptive features via multi-task training with timestamp and class label prediction. In adaptive learning, VIBE utilizes retrieved unlabeled data from online streams created posterior to training data time. Substantial Twitter experiments on three classification tasks show that our model, with only 3% of data, significantly outperforms previous state-of-the-art continued-pretraining methods.

We present a systematic study and comprehensive evaluation of large language models for automatic multilingual readability assessment. In particular, we construct ReadMe++, a multilingual multi-domain dataset with human annotations of 9757 sentences in Arabic, English, French, Hindi, and Russian collected from 112 different data sources. ReadMe++ offers more domain and language diversity than existing readability datasets, making it ideal for benchmarking multilingual and non-English language models (including mBERT, XLM-R, mT5, Llama-2, GPT-4, etc.) in the supervised, unsupervised, and few-shot prompting settings. Our experiments reveal that models fine-tuned on ReadMe++ outperform those trained on single-domain datasets, showcasing superior performance on multi-domain readability assessment and cross-lingual transfer capabilities. We also compare to traditional readability metrics (such as Flesch-Kincaid Grade Level and Open Source Metric for Measuring Arabic Narratives), as well as the state-of-the-art unsupervised metric RSRS (Martinc et al., 2021). We will make our data and code publicly available at: https://github.com/tareknaous/readme.

Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such approaches fall short when applied to dynamic scenarios or domains, whereas a new type of knowledge emerges. This necessitates a system that can handle evolving schema automatically to extract information for KGC. To address this need, we propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training. We first split and convert existing datasets based on three principles to build a benchmark, i.e., horizontal schema expansion, vertical schema expansion, and hybrid schema expansion; then investigate the schema-adaptable performance of several well-known approaches such as Text2Event, TANL, UIE and GPT-3.5. We further propose a simple yet effective baseline dubbed \textsc{AdaKGC}, which contains schema-enriched prefix instructor and schema-conditioned dynamic decoding to better handle evolving schema. Comprehensive experimental results illustrate that AdaKGC can outperform baselines but still have room for improvement. We hope the proposed work can deliver benefits to the community. Code and datasets available at https://github.com/zjunlp/AdaKGC.

Large language models have revolutionized the field of artificial intelligence and have been used in various applications. Among these models, ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI, it stands out as a powerful tool that has been widely adopted. ChatGPT has been successfully applied in numerous areas, including chatbots, content generation, language translation, personalized recommendations, and even medical diagnosis and treatment. Its success in these applications can be attributed to its ability to generate human-like responses, understand natural language, and adapt to different contexts. Its versatility and accuracy make it a powerful tool for natural language processing (NLP). However, there are also limitations to ChatGPT, such as its tendency to produce biased responses and its potential to perpetuate harmful language patterns. This article provides a comprehensive overview of ChatGPT, its applications, advantages, and limitations. Additionally, the paper emphasizes the importance of ethical considerations when using this robust tool in real-world scenarios. Finally, This paper contributes to ongoing discussions surrounding artificial intelligence and its impact on vision and NLP domains by providing insights into prompt engineering techniques.

Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to operators due to the violations and the invasions of the restricted airspaces. A cost-effective and real-time framework is needed to detect the presence of drones in such cases. In this contribution, we propose an efficient radio frequency-based detection framework called URANUS. We leverage real-time data provided by the Radio Frequency/Direction Finding system, and radars in order to detect, classify and identify drones (multi-copter and fixed-wings) invading no-drone zones. We adopt a Multilayer Perceptron neural network to identify and classify UAVs in real-time, with $90$% accuracy. For the tracking task, we use a Random Forest model to predict the position of a drone with an MSE $\approx0.29$, MAE $\approx0.04$, and $R^2\approx 0.93$. Furthermore, coordinate regression is performed using Universal Transverse Mercator coordinates to ensure high accuracy. Our analysis shows that URANUS is an ideal framework for identifying, classifying, and tracking UAVs that most Critical Infrastructure operators can adopt.

Besides its common use cases in epidemiology, political, and social sciences, causality turns out to be crucial in evaluating the fairness of automated decisions, both in a legal and everyday sense. We provide arguments and examples, of why causality is particularly important for fairness evaluation. In particular, we point out the social impact of non-causal predictions and the legal anti-discrimination process that relies on causal claims. We conclude with a discussion about the challenges and limitations of applying causality in practical scenarios as well as possible solutions.

This study presents German FinBERT, a novel pre-trained German language model tailored for financial textual data. The model is trained through a comprehensive pre-training process, leveraging a substantial corpus comprising financial reports, ad-hoc announcements and news related to German companies. The corpus size is comparable to the data sets commonly used for training standard BERT models. I evaluate the performance of German FinBERT on downstream tasks, specifically sentiment prediction, topic recognition and question answering against generic German language models. My results demonstrate improved performance on finance-specific data, indicating the efficacy of German FinBERT in capturing domain-specific nuances. The presented findings suggest that German FinBERT holds promise as a valuable tool for financial text analysis, potentially benefiting various applications in the financial domain.

David DePape, the man accused of attempting to kidnap former U.S. House Speaker Nancy Pelosi and attacking her husband last year after breaking into the couple's home, detailed his descent into political extremism and belief in conspiracy theories during his testimony Tuesday in the federal trial against him. DePape, 43, explained that long before he broke into the Pelosis' San Francisco home in the early morning of Oct. 28, 2022, with plans to get the lawmaker to fess up to corruption within the Democratic Party, he considered himself to be left-leaning politically. He thought Sept. 11 was an "inside job" during former President George W. Bush's tenure in the White House, he said, and at one point he maintained a "very strong anti-Trump bias," believing the former president was a puppet of the ruling class and Wall Street. That all changed as DePape spent entire days on the internet watching YouTube and listening to right-leaning political podcasts while playing video games in a garage he called home in Richmond, north of Oakland. His favorite podcasters included right-wing pundit Tim Pool and James Lindsay, a conservative influencer that the nonprofit advocacy organization Southern Poverty Law Center describes as a promoter of anti-LGBTQ rhetoric and "conspiracy theories about the supposed communist takeover of the world."