Binding precedents (S\'umulas Vinculantes) constitute a juridical instrument unique to the Brazilian legal system and whose objectives include the protection of the Federal Supreme Court against repetitive demands. Studies of the effectiveness of these instruments in decreasing the Court's exposure to similar cases, however, indicate that they tend to fail in such a direction, with some of the binding precedents seemingly creating new demands. We empirically assess the legal impact of five binding precedents, 11, 14, 17, 26 and 37, at the highest court level through their effects on the legal subjects they address. This analysis is only possible through the comparison of the Court's ruling about the precedents' themes before they are created, which means that these decisions should be detected through techniques of Similar Case Retrieval. The contributions of this article are therefore twofold: on the mathematical side, we compare the uses of different methods of Natural Language Processing -- TF-IDF, LSTM, BERT, and regex -- for Similar Case Retrieval, whereas on the legal side, we contrast the inefficiency of these binding precedents with a set of hypotheses that may justify their repeated usage. We observe that the deep learning models performed significantly worse in the specific Similar Case Retrieval task and that the reasons for binding precedents to fail in responding to repetitive demand are heterogeneous and case-dependent, making it impossible to single out a specific cause.

While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training on bias mitigation, is lacking. This study addresses this gap by systematically training six LLMs of identical size (2.6B parameters) and architecture: five monolingual models (English, German, French, Italian, and Spanish) and one multilingual model trained on an equal distribution of data across these languages, all using publicly available data. To ensure robust evaluation, standard bias benchmarks were automatically translated into the five target languages and verified for both translation quality and bias preservation by human annotators. Our results consistently demonstrate that multilingual training effectively mitigates bias. Moreover, we observe that multilingual models achieve not only lower bias but also superior prediction accuracy when compared to monolingual models with the same amount of training data, model architecture, and size.

Evaluating the degree of reproduction of copyright-protected content by language models (LMs) is of significant interest to the AI and legal communities. Although both literal and non-literal similarities are considered by courts when assessing the degree of reproduction, prior research has focused only on literal similarities. To bridge this gap, we introduce CopyBench, a benchmark designed to measure both literal and non-literal copying in LM generations. Using copyrighted fiction books as text sources, we provide automatic evaluation protocols to assess literal and non-literal copying, balanced against the model utility in terms of the ability to recall facts from the copyrighted works and generate fluent completions. We find that, although literal copying is relatively rare, two types of non-literal copying -- event copying and character copying -- occur even in models as small as 7B parameters. Larger models demonstrate significantly more copying, with literal copying rates increasing from 0.2% to 10.5% and non-literal copying from 2.3% to 6.9% when comparing Llama3-8B and 70B models, respectively. We further evaluate the effectiveness of current strategies for mitigating copying and show that (1) training-time alignment can reduce literal copying but may increase non-literal copying, and (2) current inference-time mitigation methods primarily reduce literal but not non-literal copying.

On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest owing to their superior privacy, reduced latency, and bandwidth saving. Nonetheless, the capabilities of on-device LLMs are intrinsically constrained by the limited capacity of edge devices compared to the much more powerful cloud centers. To bridge the gap between cloud-based and on-device AI, mobile edge intelligence (MEI) presents a viable solution to this problem by provisioning AI capabilities within the edge of mobile networks with improved privacy and latency relative to cloud computing. MEI sits between on-device AI and cloud-based AI, featuring wireless communications and more powerful computing resources than end devices. This article provides a contemporary survey on harnessing MEI for LLMs. We first cover the preliminaries of LLMs, starting with LLMs and MEI, followed by resource-efficient LLM techniques. We then illustrate several killer applications to demonstrate the need for deploying LLMs at the network edge and present an architectural overview of MEI for LLMs (MEI4LLM). Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We aim to inspire researchers in the field to leverage mobile edge computing to facilitate LLM deployment in close proximity to users, thereby unleashing the potential of LLMs across various privacy- and delay-sensitive applications.

This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required. To ensure their responsible use and implementation, both developers and users of research incorporating these tools, should familiarize themselves with their current limitations, including the issues related to scientific validity, risk of bias, and consider equity and ethical implications. We also surveyed the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools.

With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes domains, ensuring the trustworthiness, safety, and observability of these systems has become crucial. It is essential to evaluate and monitor AI systems not only for accuracy and quality-related metrics but also for robustness, bias, security, interpretability, and other responsible AI dimensions. We focus on large language models (LLMs) and other generative AI models, which present additional challenges such as hallucinations, harmful and manipulative content, and copyright infringement. In this survey article accompanying our KDD 2024 tutorial, we highlight a wide range of harms associated with generative AI systems, and survey state of the art approaches (along with open challenges) to address these harms.

Are large language models (LLMs) biased towards text generated by LLMs over text authored by humans, leading to possible anti-human bias? Utilizing a classical experimental design inspired by employment discrimination studies, we tested widely-used LLMs, including GPT-3.5 and GPT4, in binary-choice scenarios. These involved LLM-based agents selecting between products and academic papers described either by humans or LLMs under identical conditions. Our results show a consistent tendency for LLM-based AIs to prefer LLM-generated content. This suggests the possibility of AI systems implicitly discriminating against humans, giving AI agents an unfair advantage.

We introduce WildGuard -- an open, light-weight moderation tool for LLM safety that achieves three goals: (1) identifying malicious intent in user prompts, (2) detecting safety risks of model responses, and (3) determining model refusal rate. Together, WildGuard serves the increasing needs for automatic safety moderation and evaluation of LLM interactions, providing a one-stop tool with enhanced accuracy and broad coverage across 13 risk categories. While existing open moderation tools such as Llama-Guard2 score reasonably well in classifying straightforward model interactions, they lag far behind a prompted GPT-4, especially in identifying adversarial jailbreaks and in evaluating models' refusals, a key measure for evaluating safety behaviors in model responses. To address these challenges, we construct WildGuardMix, a large-scale and carefully balanced multi-task safety moderation dataset with 92K labeled examples that cover vanilla (direct) prompts and adversarial jailbreaks, paired with various refusal and compliance responses. WildGuardMix is a combination of WildGuardTrain, the training data of WildGuard, and WildGuardTest, a high-quality human-annotated moderation test set with 5K labeled items covering broad risk scenarios. Through extensive evaluations on WildGuardTest and ten existing public benchmarks, we show that WildGuard establishes state-of-the-art performance in open-source safety moderation across all the three tasks compared to ten strong existing open-source moderation models (e.g., up to 26.4% improvement on refusal detection). Importantly, WildGuard matches and sometimes exceeds GPT-4 performance (e.g., up to 3.9% improvement on prompt harmfulness identification). WildGuard serves as a highly effective safety moderator in an LLM interface, reducing the success rate of jailbreak attacks from 79.8% to 2.4%.

As Large language models have shown a remarkable a significant milestone in this area, Elhage et al. ability to learn and perform complex tasks through (2021) demonstrated the existence of induction in-context learning (ICL) (Brown et al., 2020; Touvron heads in Transformer LMs. These heads scan the et al., 2023b). In ICL, the model receives context for previous instances of the current token a demonstration context and a query question as using a prefix matching mechanism, which identifies a prompt for prediction. Unlike supervised learning, if and where a token has appeared before. ICL utilises the pretrained model's capabilities If a matching token is found, the head employs to recognise and replicate patterns within the a copying mechanism to increase the probability demonstration context, thereby enabling accurate of the subsequent token, facilitating exact or approximate predictions for the query without the use of gradient repetition of sequences and embodying updates.

Since its implementation in May 2018, the General Data Protection Regulation (GDPR) has prompted businesses to revisit and revise their data handling practices to ensure compliance. The privacy policy, which serves as the primary means of informing users about their privacy rights and the data practices of companies, has been significantly updated by numerous businesses post-GDPR implementation. However, many privacy policies remain packed with technical jargon, lengthy explanations, and vague descriptions of data practices and user rights. This makes it a challenging task for users and regulatory authorities to manually verify the GDPR compliance of these privacy policies. In this study, we aim to address the challenge of compliance analysis between GDPR (Article 13) and privacy policies for 5G networks. We manually collected privacy policies from almost 70 different 5G MNOs, and we utilized an automated BERT-based model for classification. We show that an encouraging 51$\%$ of companies demonstrate a strong adherence to GDPR. In addition, we present the first study that provides current empirical evidence on the readability of privacy policies for 5G network. we adopted readability analysis toolset that incorporates various established readability metrics. The findings empirically show that the readability of the majority of current privacy policies remains a significant challenge. Hence, 5G providers need to invest considerable effort into revising these documents to enhance both their utility and the overall user experience.