The rapid advancement of Generative Artificial Intelligence (GenAI) across diverse sectors raises significant environmental concerns, notably the carbon emissions from their cloud and high performance computing (HPC) infrastructure. This paper presents Sprout, an innovative framework designed to address these concerns by reducing the carbon footprint of generative Large Language Model (LLM) inference services. Sprout leverages the innovative concept of "generation directives" to guide the autoregressive generation process, thereby enhancing carbon efficiency. Our proposed method meticulously balances the need for ecological sustainability with the demand for high-quality generation outcomes. Employing a directive optimizer for the strategic assignment of generation directives to user prompts and an original offline quality evaluator, Sprout demonstrates a significant reduction in carbon emissions by over 40% in real-world evaluations using the Llama2 LLM and global electricity grid data. This research marks a critical step toward aligning AI technology with sustainable practices, highlighting the potential for mitigating environmental impacts in the rapidly expanding domain of generative artificial intelligence.

The increasing integration of electric vehicles (EVs) into the grid can pose a significant risk to the distribution system operation in the absence of coordination. In response to the need for effective coordination of EVs within the distribution network, this paper presents a safety-aware reinforcement learning (RL) algorithm designed to manage EV charging stations while ensuring the satisfaction of system constraints. Unlike existing methods, our proposed algorithm does not rely on explicit penalties for constraint violations, eliminating the need for penalty coefficient tuning. Furthermore, managing EV charging stations is further complicated by multiple uncertainties, notably the variability in solar energy generation and energy prices. To address this challenge, we develop an off-policy RL algorithm to efficiently utilize data to learn patterns in such uncertain environments. Our algorithm also incorporates a maximum entropy framework to enhance the RL algorithm's exploratory process, preventing convergence to local optimal solutions. Simulation results demonstrate that our algorithm outperforms traditional RL algorithms in managing EV charging in the distribution network.

A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources better. Such approaches are transitioning towards leveraging social media conversations to analyze the needs of communities and the assets already present within them. However, manual analysis of exponentially increasing social media conversations is challenging. There is a gap in the present literature in computationally analyzing how community members discuss the strengths and needs of the community. To address this gap, we introduce the task of identifying, extracting, and categorizing community needs and assets from conversational data using sophisticated natural language processing methods. To facilitate this task, we introduce the first dataset about community needs and assets consisting of 3,511 conversations from Reddit, annotated using crowdsourced workers. Using this dataset, we evaluate an utterance-level classification model compared to sentiment classification and a popular large language model (in a zero-shot setting), where we find that our model outperforms both baselines at an F1 score of 94% compared to 49% and 61% respectively. Furthermore, we observe through our study that conversations about needs have negative sentiments and emotions, while conversations about assets focus on location and entities. The dataset is available at https://github.com/towhidabsar/CommunityNeeds.

Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations. The primary overhead of GNN update stems from graph propagation and weight transformation, both involving operations on graph-scale matrices. Previous studies attempt to reduce the computational budget by leveraging graph-level or network-level sparsification techniques, resulting in downsized graph or weights. In this work, we propose Unifews, which unifies the two operations in an entry-wise manner considering individual matrix elements, and conducts joint edge-weight sparsification to enhance learning efficiency. The entry-wise design of Unifews enables adaptive compression across GNN layers with progressively increased sparsity, and is applicable to a variety of architectural designs with on-the-fly operation simplification. Theoretically, we establish a novel framework to characterize sparsified GNN learning in view of a graph optimization process, and prove that Unifews effectively approximates the learning objective with bounded error and reduced computational load. We conduct extensive experiments to evaluate the performance of our method in diverse settings. Unifews is advantageous in jointly removing more than 90% of edges and weight entries with comparable or better accuracy than baseline models. The sparsification offers remarkable efficiency improvements including 10-20x matrix operation reduction and up to 100x acceleration in graph propagation time for the largest graph at the billion-edge scale.

Cross-lingual summarization (XLS) generates summaries in a language different from that of the input documents (e.g., English to Spanish), allowing speakers of the target language to gain a concise view of their content. In the present day, the predominant approach to this task is to take a performing, pretrained multilingual language model (LM) and fine-tune it for XLS on the language pairs of interest. However, the scarcity of fine-tuning samples makes this approach challenging in some cases. For this reason, in this paper we propose revisiting the summarize-and-translate pipeline, where the summarization and translation tasks are performed in a sequence. This approach allows reusing the many, publicly-available resources for monolingual summarization and translation, obtaining a very competitive zero-shot performance. In addition, the proposed pipeline is completely differentiable end-to-end, allowing it to take advantage of few-shot fine-tuning, where available. Experiments over two contemporary and widely adopted XLS datasets (CrossSum and WikiLingua) have shown the remarkable zero-shot performance of the proposed approach, and also its strong few-shot performance compared to an equivalent multilingual LM baseline, that the proposed approach has been able to outperform in many languages with only 10% of the fine-tuning samples.

Modern Neural Machine Translation systems exhibit strong performance in several different languages and are constantly improving. Their ability to learn continuously is, however, still severely limited by the catastrophic forgetting issue. In this work, we leverage a key property of encoder-decoder Transformers, i.e. their generative ability, to propose a novel approach to continually learning Neural Machine Translation systems. We show how this can effectively learn on a stream of experiences comprising different languages, by leveraging a replay memory populated by using the model itself as a generator of parallel sentences. We empirically demonstrate that our approach can counteract catastrophic forgetting without requiring explicit memorization of training data. Code will be publicly available upon publication. Code: https://github.com/m-resta/sg-rep

Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning. The primary obstacle in this task stems from the inherent incompleteness of KGs. Existing research has predominantly focused on addressing the issue of missing edges in KGs, thereby neglecting another aspect of incompleteness: the emergence of new entities. Furthermore, most of the existing methods tend to reason over each logical operator separately, rather than comprehensively analyzing the query as a whole during the reasoning process. In this paper, we propose a query-aware prompt-fused framework named Pro-QE, which could incorporate existing query embedding methods and address the embedding of emerging entities through contextual information aggregation. Additionally, a query prompt, which is generated by encoding the symbolic query, is introduced to gather information relevant to the query from a holistic perspective. To evaluate the efficacy of our model in the inductive setting, we introduce two new challenging benchmarks. Experimental results demonstrate that our model successfully handles the issue of unseen entities in logical queries. Furthermore, the ablation study confirms the efficacy of the aggregator and prompt components.

Detecting fake news has received a lot of attention. Many previous methods concatenate independently encoded unimodal data, ignoring the benefits of integrated multimodal information. Also, the absence of specialized feature extraction for text and images further limits these methods. This paper introduces an end-to-end model called TT-BLIP that applies the bootstrapping language-image pretraining for unified vision-language understanding and generation (BLIP) for three types of information: BERT and BLIP\textsubscript{Txt} for text, ResNet and BLIP\textsubscript{Img} for images, and bidirectional BLIP encoders for multimodal information. The Multimodal Tri-Transformer fuses tri-modal features using three types of multi-head attention mechanisms, ensuring integrated modalities for enhanced representations and improved multimodal data analysis. The experiments are performed using two fake news datasets, Weibo and Gossipcop. The results indicate TT-BLIP outperforms the state-of-the-art models.

Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly from data. This approach has achieved impressive results and has contributed significantly to the progress of AI, particularly in the sphere of supervised deep learning. It has also simplified the design of machine learning systems as the learning process is highly automated. However, not all data processing tasks in conventional deep learning pipelines have been automated. In most cases data has to be manually collected, preprocessed and further extended through data augmentation before they can be effective for training. Recently, special techniques for automating these tasks have emerged. The automation of data processing tasks is driven by the need to utilize large volumes of complex, heterogeneous data for machine learning and big data applications. Today, end-to-end automated data processing systems based on automated machine learning (AutoML) techniques are capable of taking raw data and transforming them into useful features for Big Data tasks by automating all intermediate processing stages. In this work, we present a thorough review of approaches for automating data processing tasks in deep learning pipelines, including automated data preprocessing--e.g., data cleaning, labeling, missing data imputation, and categorical data encoding--as well as data augmentation (including synthetic data generation using generative AI methods) and feature engineering--specifically, automated feature extraction, feature construction and feature selection. In addition to automating specific data processing tasks, we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine learning pipeline.

The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collection of documents detailing air travel regulations. The most relevant passages from these documents are presented along with links to the original documents and the generated queries, enabling users to dissect and leverage the information for their unique circumstances. The system successfully overcomes two predominant challenges: understanding complex user inputs, and delivering accurate answers, free of hallucinations, that passengers can rely on for making informed decisions. A user study comparing the chatbot to a Google search demonstrated the chatbot's usefulness and ease of use. Beyond the primary goal of providing accurate and timely information to air passengers regarding their rights, we hope that this system will also enable further research exploring the tradeoff between the user-friendly conversational interface of chatbots and the accuracy of retrieval systems.