Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessary include the target series. In the present study, we investigate the performance of this special case of STLF, called transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series and assist TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.

Positron emission tomography (PET) is a widely used, highly sensitive molecular imaging in clinical diagnosis. There is interest in reducing the radiation exposure from PET but also maintaining adequate image quality. Recent methods using convolutional neural networks (CNNs) to generate synthesized high-quality PET images from low-dose counterparts have been reported to be state-of-the-art for low-to-high image recovery methods. However, these methods are prone to exhibiting discrepancies in texture and structure between synthesized and real images. Furthermore, the distribution shift between low-dose PET and standard PET has not been fully investigated. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN). We introduce (1) An adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input, and (2) A self-supervised pre-training strategy to enhance the feature representation of the coarse generator. Our experiments with a public benchmark dataset of total-body PET images show that SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.

We study the ability of state-of-the art models to answer constraint satisfaction queries for information retrieval (e.g., 'a list of ice cream shops in San Diego'). In the past, such queries were considered to be tasks that could only be solved via web-search or knowledge bases. More recently, large language models (LLMs) have demonstrated initial emergent abilities in this task. However, many current retrieval benchmarks are either saturated or do not measure constraint satisfaction. Motivated by rising concerns around factual incorrectness and hallucinations of LLMs, we present KITAB, a new dataset for measuring constraint satisfaction abilities of language models. KITAB consists of book-related data across more than 600 authors and 13,000 queries, and also offers an associated dynamic data collection and constraint verification approach for acquiring similar test data for other authors. Our extended experiments on GPT4 and GPT3.5 characterize and decouple common failure modes across dimensions such as information popularity, constraint types, and context availability. Results show that in the absence of context, models exhibit severe limitations as measured by irrelevant information, factual errors, and incompleteness, many of which exacerbate as information popularity decreases. While context availability mitigates irrelevant information, it is not helpful for satisfying constraints, identifying fundamental barriers to constraint satisfaction. We open source our contributions to foster further research on improving constraint satisfaction abilities of future models.

Out of policy speech (OOPS) has permeated social media with serious consequences for both individuals and society. Although it comprises a small fraction of the content generated daily on social media, sifting through the data to quickly identify and eliminate the toxic content is difficult. The scale of this problem has long passed a threshold that requires automated detection. Yet it remains to be a challenging problem for machine learning (ML) due to the way OOPS manifests itself in datasets: context-dependent, nuanced, non-colloquial language that may even be syntactically incorrect. Because the OOPS content of the dataset is usually only a small fraction of the overall size, there is a high imbalance between OOPS and in-policy text. Related to this, there are not many high-quality labeled datasets with consistent definitions of OOPS and in-policy content. The difficulties are exacerbated further by significant differences in the distributions of the datasets that the model has been trained on and the data it sees during deployment. When faced with all of these challenges, ML models applied to natural language processing (NLP) tasks quickly reach a performance ceiling that limits their usefulness for sensitive tasks, such as OOPS detection.

Providing explainable and faithful feedback is crucial for automated student answer assessment. In this paper, we introduce a novel framework that explores using ChatGPT, a cutting-edge large language model, for the concurrent tasks of student answer scoring and rationale generation. We identify the appropriate instructions by prompting ChatGPT with different templates to collect the rationales, where inconsistent rationales are refined to align with marking standards. The refined ChatGPT outputs enable us to fine-tune a smaller language model that simultaneously assesses student answers and provides rationales. Extensive experiments on the benchmark dataset show that the proposed method improves the overall QWK score by 11% compared to ChatGPT. Furthermore, our thorough analysis and human evaluation demonstrate that the rationales generated by our proposed method are comparable to those of ChatGPT. Our approach provides a viable solution to achieve explainable automated assessment in education. Code available at https://github.com/lijiazheng99/aera.

Performing inference on large volumes of samples with large language models (LLMs) can be computationally and financially costly in industry and real-world use. We propose batch prompting, a simple yet effective prompting approach that enables the LLM to run inference in batches, instead of one sample at a time. Our method reduces both token and time costs while retaining downstream performance. We theoretically demonstrate that under a few-shot in-context learning setting, the inference costs decrease almost inverse linearly with the number of samples in each batch. We extensively validate the effectiveness of batch prompting on ten datasets across commonsense QA, arithmetic reasoning, and NLI/NLU: batch prompting significantly~(up to 5x with six samples in batch) reduces the LLM (Codex) inference token and time costs while achieving better or comparable performance. For state-of-the-art Chat-based LLMs, e.g., GPT-3.5 and GPT-4, we show the benefits of batch prompting also hold. Further analysis shows that the number of samples in each batch and the complexity of tasks affect its performance. Moreover, batch prompting can be applied across different reasoning methods using LLMs. Our code can be found at the site https://github.com/xlang-ai/batch-prompting.

Large language models exhibit surprising emergent generalization properties, yet also struggle on many simple reasoning tasks such as arithmetic and parity. This raises the question of if and when Transformer models can learn the true algorithm for solving a task. We study the scope of Transformers' abilities in the specific setting of length generalization on algorithmic tasks. Here, we propose a unifying framework to understand when and how Transformers can exhibit strong length generalization on a given task. Specifically, we leverage RASP (Weiss et al., 2021) -- a programming language designed for the computational model of a Transformer -- and introduce the RASP-Generalization Conjecture: Transformers tend to length generalize on a task if the task can be solved by a short RASP program which works for all input lengths. This simple conjecture remarkably captures most known instances of length generalization on algorithmic tasks. Moreover, we leverage our insights to drastically improve generalization performance on traditionally hard tasks (such as parity and addition). On the theoretical side, we give a simple example where the "min-degree-interpolator" model of learning from Abbe et al. (2023) does not correctly predict Transformers' out-of-distribution behavior, but our conjecture does. Overall, our work provides a novel perspective on the mechanisms of compositional generalization and the algorithmic capabilities of Transformers.

One of the most gruelling hurdles in any new relationship is when it becomes time to meet the parents. But now Tinder has come up with a way to make sure your partner has the familial seal of approval before they've even been introduced. The dating app has created a tool called Matchmaker, which allows users to offer up to 15 friends, family members or guardians 24 hours to scrutinise their possible matches. They can view the profiles and make suggestions without having an account of their own – and, fortunately, cannot start messaging on your behalf. Once the session ends, Tinder users can review the profiles recommended by their matchmakers before making a final decision on whether or not they see them as a good fit.

It's a question from the parents that every singleton dreads: are you dating anyone at the moment? But the days of dismissing their questioning could be a thing of the past, thanks to Tinder's latest feature. The dating app has launched Tinder Matchmaker, which lets your friends and fmaily view and suggest potential matches for you. 'For years, singles have asked their friends to help find their next match on Tinder,' said Melissa Hobley, Chief Marketing Officer at Tinder. 'Tinder Matchmaker brings your circle of trust into your dating journey and helps you see the possibilities you might be overlooking from the perspective of those closest to you.'

Voice actor Armando Plata does not recall promoting a shopping mall in Bogota, narrating a porn movie or advertizing a big bank. Yet his voice comes over loud and clear: schmoozing, sighing and selling with neither permission nor payment. It was the mild, robotic twang -- rather than worry over any memory lapse -- that alerted Plata to the fact his voice had been quietly cloned via artificial intelligence, robbing the veteran actor of his key asset, artistic choice and vocal rights. "I believe that the most cloned and artificially used voice in Spanish is mine," said Plata, owner of a deep and lilting voice, 50-year audio career and president of the Colombian Association of Voice Actors.