Aligning language models with human values is crucial, especially as they become more integrated into everyday life. While models are often adapted to user preferences, it is equally important to ensure they align with moral norms and behaviours in real-world social situations. Despite significant progress in languages like English and Chinese, French has seen little attention in this area, leaving a gap in understanding how LLMs handle moral reasoning in this language. To address this gap, we introduce Histoires Morales, a French dataset derived from Moral Stories, created through translation and subsequently refined with the assistance of native speakers to guarantee grammatical accuracy and adaptation to the French cultural context. We also rely on annotations of the moral values within the dataset to ensure their alignment with French norms. Histoires Morales covers a wide range of social situations, including differences in tipping practices, expressions of honesty in relationships, and responsibilities toward animals. To foster future research, we also conduct preliminary experiments on the alignment of multilingual models on French and English data and the robustness of the alignment. We find that while LLMs are generally aligned with human moral norms by default, they can be easily influenced with user-preference optimization for both moral and immoral data.

Interpretability in reinforcement learning is crucial for ensuring AI systems align with human values and fulfill the diverse related requirements including safety, robustness and fairness. Building on recent approaches to encouraging sparsity and locality in neural networks, we demonstrate how the penalisation of non-local weights leads to the emergence of functionally independent modules in the policy network of a reinforcement learning agent. To illustrate this, we demonstrate the emergence of two parallel modules for assessment of movement along the X and Y axes in a stochastic Minigrid environment. Through the novel application of community detection algorithms, we show how these modules can be automatically identified and their functional roles verified through direct intervention on the network weights prior to inference. This establishes a scalable framework for reinforcement learning interpretability through functional modularity, addressing challenges regarding the trade-off between completeness and cognitive tractability of reinforcement learning explanations.

Rainfall prediction remains a persistent challenge due to the highly nonlinear and complex nature of meteorological data. Existing approaches lack systematic utilization of grid search for optimal hyperparameter tuning, relying instead on heuristic or manual selection, frequently resulting in sub-optimal results. Additionally, these methods rarely incorporate newly constructed meteorological features such as differences between temperature and humidity to capture critical weather dynamics. Furthermore, there is a lack of systematic evaluation of ensemble learning techniques and limited exploration of diverse advanced models introduced in the past one or two years. To address these limitations, we propose a robust ensemble learning grid search-tuned framework (RAINER) for rainfall prediction. RAINER incorporates a comprehensive feature engineering pipeline, including outlier removal, imputation of missing values, feature reconstruction, and dimensionality reduction via Principal Component Analysis (PCA). The framework integrates novel meteorological features to capture dynamic weather patterns and systematically evaluates non-learning mathematical-based methods and a variety of machine learning models, from weak classifiers to advanced neural networks such as Kolmogorov-Arnold Networks (KAN). By leveraging grid search for hyperparameter tuning and ensemble voting techniques, RAINER achieves promising results within real-world datasets.

The 'keyword method' is an effective technique for learning vocabulary of a foreign language. It involves creating a memorable visual link between what a word means and what its pronunciation in a foreign language sounds like in the learner's native language. However, these memorable visual links remain implicit in the people's mind and are not easy to remember for a large set of words. To enhance the memorisation and recall of the vocabulary, we developed an application that combines the keyword method with text-to-image generators to externalise the memorable visual links into visuals. These visuals represent additional stimuli during the memorisation process. To explore the effectiveness of this approach we first run a pilot study to investigate how difficult it is to externalise the descriptions of mental visualisations of memorable links, by asking participants to write them down. We used these descriptions as prompts for text-to-image generator (DALL-E2) to convert them into images and asked participants to select their favourites. Next, we compared different text-to-image generators (DALL-E2, Midjourney, Stable and Latent Diffusion) to evaluate the perceived quality of the generated images by each. Despite heterogeneous results, participants mostly preferred images generated by DALL-E2, which was used also for the final study. In this study, we investigated whether providing such images enhances the retention of vocabulary being learned, compared to the keyword method only. Our results indicate that people did not encounter difficulties describing their visualisations of memorable links and that providing corresponding images significantly improves memory retention.

The state-of-the-art online learning models generally conduct a single online gradient descent when a new sample arrives and thus suffer from suboptimal model weights. To this end, we introduce an online broad learning system framework with closed-form solutions for each online update. Different from employing existing incremental broad learning algorithms for online learning tasks, which tend to incur degraded accuracy and expensive online update overhead, we design an effective weight estimation algorithm and an efficient online updating strategy to remedy the above two deficiencies, respectively. Specifically, an effective weight estimation algorithm is first developed by replacing notorious matrix inverse operations with Cholesky decomposition and forward-backward substitution to improve model accuracy. Second, we devise an efficient online updating strategy that dramatically reduces online update time. Theoretical analysis exhibits the splendid error bound and low time complexity of our model. The most popular test-then-training evaluation experiments on various real-world datasets prove its superiority and efficiency. Furthermore, our framework is naturally extended to data stream scenarios with concept drift and exceeds state-of-the-art baselines.

Modern machine learning produces models that are impossible for users or developers to fully understand -- raising concerns about trust, oversight and human dignity. Transparency and explainability methods aim to provide some help in understanding models, but it remains challenging for developers to design explanations that are understandable to target users and effective for their purpose. Emerging guidelines and regulations set goals but may not provide effective actionable guidance to developers. In a controlled experiment with 124 participants, we investigate whether and how specific forms of policy guidance help developers design explanations for an ML-powered screening tool for diabetic retinopathy. Contrary to our expectations, we found that participants across the board struggled to produce quality explanations, comply with the provided policy requirements for explainability, and provide evidence of compliance. We posit that participant noncompliance is in part due to a failure to imagine and anticipate the needs of their audience, particularly non-technical stakeholders. Drawing on cognitive process theory and the sociological imagination to contextualize participants' failure, we recommend educational interventions.

This paper focuses on the piloting of the CGScholar AI Helper, a Generative AI (GenAI) assistant tool that aims to provide feedback on writing in high school contexts. The aim was to use GenAI to provide formative and summative feedback on students' texts in English Language Arts (ELA) and History. The trials discussed in this paper relate to Grade 11, a crucial learning phase when students are working towards college readiness. These trials took place in two very different schools in the Midwest of the United States, one in a low socio-economic background with low-performance outcomes and the other in a high socio-economic background with high-performance outcomes. The assistant tool used two main mechanisms "prompt engineering" based on participant teachers' assessment rubric and "fine-tuning" a Large Language Model (LLM) from a customized corpus of teaching materials using Retrieval Augmented Generation (RAG). This paper focuses on the CGScholar AI Helper's potential to enhance students' writing abilities and support teachers in ELA and other subject areas requiring written assignments.

First, the main class of losses that the paper introduces, that of relative Lipschitz continuity (Def. In particular, given that the losses are (RLC) then one can recover relative Lipschitz continuity via a direct combination of convexity and Cauchy-Schwartz inequality. Moreover, conversely every relative Lipschitz continuous loss can be seen as (RLC) if one chooses the respective Riemannian metric accordingly; this becomes even more evident for the example that the paper presents, if f(x) x {2} for x\in R, then one can straightforwardly choose the Riemannian metric in such a manner that the respective dual norm would be \ v\ _{x,\ast} v /x and (RLC) follows. That said, this weakens significantly the contributions concerning FTRL and the like, since in Antonakopoulos et. On the other hand, concerning the most intriguing part that of establishing logarithmic regret for the case where the loss functions are in addition relatively strongly convex, there is no obvious way to establish any relevant examples that satisfy simultaneously relative Lipschitz continuity and relative strong convexity, besides of course the euclidean ones.

This paper treats the problem of online convex optimization without Lipschitz continuity of the loss functions. The authors consider a variant of Lipschitz continuity called "relative Lipschitz continuity": this notion is originally due to Lu (2019) and involves a Bregman divergence instead of the standard norm in comparing nearby points. In this context, the authors prove the following results: - Under only relative Lipschitz continuity: an O(sqrt{T}) regret bound for follow-the-regularized-leader (FTRL) and a "stabilized" variant of the online mirror descent (OMD) algorithm. These results are similar to standard bounds in the literature for Lipschitz continuous / strongly convex functions. The extension to *relative* Lipschitz continuous / strongly convex functions was welcomed by the reviewers, but two major issues were identified: 1. An earlier ICLR paper by Antonakopoulos et al. (2020) already provides O(\sqrt{T}) bounds for FTRL and OMD under a closely related "Riemannian Lipschitz continuity" condition.

Summary and Contributions: Update: My initial review noted two main issues with the paper: reliance on the initial model, and the use of task labels during the test phase. The author response addresses the first question, but misses the point on the second one. And this alone is not sufficient to strongly influence my overall rating. In my understanding, several previous methods, such as LwF, iCaRL highlighted in the author response, classify samples without the knowledge of which group of classes (i.e., old or new) they belong to. In other words, they only use a single framework that can identify samples from any of the old or the new classes, without additional information.