Large Language Models (LLMs) have become central to numerous natural language processing tasks, but their vulnerabilities present significant security and ethical challenges. This systematic survey explores the evolving landscape of attack and defense techniques in LLMs. We classify attacks into adversarial prompt attack, optimized attacks, model theft, as well as attacks on application of LLMs, detailing their mechanisms and implications. Consequently, we analyze defense strategies, including prevention-based and detection-based defense methods. Although advances have been made, challenges remain to adapt to the dynamic threat landscape, balance usability with robustness, and address resource constraints in defense implementation. We highlight open problems, including the need for adaptive scalable defenses, explainable security techniques, and standardized evaluation frameworks. This survey provides actionable insights and directions for developing secure and resilient LLMs, emphasizing the importance of interdisciplinary collaboration and ethical considerations to mitigate risks in real-world applications.

This paper presents an automated one-shot bird call classification pipeline designed for rare species absent from large publicly available classifiers like BirdNET and Perch. While these models excel at detecting common birds with abundant training data, they lack options for species with only 1-3 known recordings-a critical limitation for conservationists monitoring the last remaining individuals of endangered birds. To address this, we leverage the embedding space of large bird classification networks and develop a classifier using cosine similarity, combined with filtering and denoising preprocessing techniques, to optimize detection with minimal training data. We evaluate various embedding spaces using clustering metrics and validate our approach in both a simulated scenario with Xeno-Canto recordings and a real-world test on the critically endangered tooth-billed pigeon (Didunculus strigirostris), which has no existing classifiers and only three confirmed recordings. The final model achieved 1.0 recall and 0.95 accuracy in detecting tooth-billed pigeon calls, making it practical for use in the field. This open-source system provides a practical tool for conservationists seeking to detect and monitor rare species on the brink of extinction.

As Artificial Intelligence (AI) is increasingly used in areas that significantly impact human lives, concerns about fairness and transparency have grown, especially regarding their impact on protected groups. Recently, the intersection of explainability and fairness has emerged as an important area to promote responsible AI systems. This paper explores how explainability methods can be leveraged to detect and interpret unfairness. We propose a pipeline that integrates local post-hoc explanation methods to derive fairness-related insights. During the pipeline design, we identify and address critical questions arising from the use of explanations as bias detectors such as the relationship between distributive and procedural fairness, the effect of removing the protected attribute, the consistency and quality of results across different explanation methods, the impact of various aggregation strategies of local explanations on group fairness evaluations, and the overall trustworthiness of explanations as bias detectors. Our results show the potential of explanation methods used for fairness while highlighting the need to carefully consider the aforementioned critical aspects.

Amazon is selling books marketed at people seeking techniques to manage their ADHD that claim to offer expert advice yet appear to be authored by a chatbot such as ChatGPT. Amazon's marketplace has been deluged with works produced by artificial intelligence that are easy and cheap to publish but include unhelpful or dangerous misinformation, such as shoddy travel guidebooks and mushroom foraging books that encourage risky tasting. A number of books have appeared on the online retailer's site offering guides to ADHD that also seem to be written by chatbots. The titles include Navigating ADHD in Men: Thriving with a Late Diagnosis, Men with Adult ADHD: Highly Effective Techniques for Mastering Focus, Time Management and Overcoming Anxiety and Men with Adult ADHD Diet & Fitness. Samples from eight books were examined for the Guardian by Originality.ai,

I feel a troubling kind of opacity in my brain lately--as if reality were becoming illegible, as if language were a vessel with holes in the bottom and meaning was leaking all over the floor. I sometimes look up words after I write them: does "illegible" still mean too messy to read? The day after Donald Trump's second Inauguration, my verbal cognition kept glitching: I got an e-mail from the children's-clothing company Hanna Andersson and read the name as "Hamas"; on the street, I thought "hot yoga" was "hot dogs"; on the subway, a theatre poster advertising "Jan. Ticketing" said "Jia Tolentino" to me. Even the words that I might use to more precisely describe the sensation of "losing it" elude me.

The State Bar of California announced Friday that its embattled leader, who has faced growing pressure to resign over the botched February roll out of a new bar exam, will step down in July. Leah T. Wilson, the agency's executive director, informed the Board of Trustees she will not seek another term in the position she has held on and off since 2017. She also apologized for her role in the February bar exam chaos. "Accountability is a bedrock principle for any leader," Wilson said in a statement. "At the end of the day, I am responsible for everything that occurs within the organization. Despite our best intentions, the experiences of applicants for the February Bar Exam simply were unacceptable, and I fully recognize the frustration and stress this experience caused. While there are no words to assuage those emotions, I do sincerely apologize."

The NGO appeared to accuse Israel of being behind the incident and called for Israeli ambassadors to be summoned to answer for "violation of international law, including the ongoing blockade and the bombing of our civilian vessel". The Israeli military said it was looking into reports of the attack. The Freedom Flotilla Coalition uploaded a video showing a fire on one of its ships but did not indicate whether anyone had been hurt. It said the attack appeared to have targeted the generator, which left the ship without power and at risk of sinking. The ship was 17 nautical miles (31.5 kilometres) east of Malta when it was hit.

Identifying regulatory statements in legislation is useful for developing metrics to measure the regulatory density and strictness of legislation. A computational method is valuable for scaling the identification of such statements from a growing body of EU legislation, constituting approximately 180,000 published legal acts between 1952 and 2023. Past work on extraction of these statements varies in the permissiveness of their definitions for what constitutes a regulatory statement. In this work, we provide a specific definition for our purposes based on the institutional grammar tool. We develop and compare two contrasting approaches for automatically identifying such statements in EU legislation, one based on dependency parsing, and the other on a transformer-based machine learning model. We found both approaches performed similarly well with accuracies of 80% and 84% respectively and a K alpha of 0.58. The high accuracies and not exceedingly high agreement suggests potential for combining strengths of both approaches.

We extend the formal framework of classifier models used in the legal domain. While the existing classifier framework characterises cases solely through the facts involved, legal reasoning fundamentally relies on both facts and rules, particularly the ratio decidendi. This paper presents an initial approach to incorporating sets of rules within a classifier. Our work is built on the work of Canavotto et al. (2023), which has developed the rule-based reason model of precedential constraint within a hierarchy of factors. We demonstrate how decisions for new cases can be inferred using this enriched rule-based classifier framework. Additionally, we provide an example of how the time element and the hierarchy of courts can be used in the new classifier framework.

This paper proposed an approach to automatically discovering subject dimension, action dimension, object dimension and adverbial dimension from texts to efficiently operate texts and support query in natural language. The high quality of trees guarantees that all subjects, actions, objects and adverbials and their subclass relations within texts can be represented. The independency of trees ensures that there is no redundant representation between trees. The expressiveness of trees ensures that the majority of sentences can be accessed from each tree and the rest of sentences can be accessed from at least one tree so that the tree-based search mechanism can support querying in natural language. Experiments show that the average precision, recall and F1-score of the abstraction trees constructed by the subclass relations of subject, action, object and adverbial are all greater than 80%. The application of the proposed approach to supporting query in natural language demonstrates that different types of question patterns for querying subject or object have high coverage of texts, and searching multiple trees on subject, action, object and adverbial according to the question pattern can quickly reduce search space to locate target sentences, which can support precise operation on texts.