Figure 4: Impact of the proportion of injected noise for learning Paragraph Embeddings on XSum dataset. PPLint and the PPL of the generation obtained from training PLANNER on the corresponding z at different noise level. We observed when the value of p is within (0, 0.7), there Performing a grid search on each task using diffusion models is an expensive process. However, it has been observed that an increase in the value of p leads to a deviation between the two. This could be attributed to a higher conversion error that occurs when p is excessively large. A.2 Selection of number of latent code k The parameter k determines the number of latent codes used to represent a paragraph and therefore controls the compression level. Latent codes with smaller values of k are easier to model using the diffusion model, but may struggle to accurately preserve all the information in the original text. Additionally, smaller values of k offer computational efficiency as the sequence length for the diffusion model is k. To determine the best set of latent codes, we conducted experiments using three different methods: 1) selecting the first k hidden vectors, 2) selecting the last k hidden vectors, and 3) selecting interleaving hidden vectors, one for every L k hidden vectors. The results of the ablation study are presented in Table 5. Based on our findings, we observed no significant difference among the different choices, so we opted for option 1). Furthermore, we discovered that increasing the value of k does not lead to a dramatic improvement in performance. To balance between efficiency and performance, in most of our study we only use k =16 Setup BLEU_clean BLEU_robust First k (k=16) 79.59 43.17 A.3 Reconstruction, denoising and interpolation examples In Table 6, we present examples that demonstrate the adeptness of the trained Variational Paragraph Embedder in providing clean and denoised reconstructions. Additionally, we showcase interpolation results (Table 7, 8) derived from two random sentences in the hotel review dataset. The interpolated paragraph is usually coherent and incorporates inputs from both sentences, characterizing the distributional smoothness of the latent space. Reconstructed text complaints: after two nights stay, i asked the maid to clean our room (empty the wastebasket & make the bed). Denoising reconstruction (hotel review), noise level 0.3 Original text * * * check out the bathroom picture * * * i was in nyc by myself to watch some friends participate in the us olympic marathon trials. Corrupted text * * [unused697] check exams the bathroom picture * * slams i was in nyc mead myself yankee 2016 some scotch ruin in the outfielder olympicnca trials.

Human commonsense understanding of the physical and social world is organized around intuitive theories. These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why? A rich literature in cognitive science has studied people's causal and moral intuitions. This work has revealed a number of factors that systematically influence people's judgments, such as the violation of norms and whether the harm is avoidable or inevitable.

The following section is answers to questions listed in datasheets for datasets. A.1 Motivation For what purpose was the dataset created? VisAlign is created to serve as a benchmark for measuring visual perception alignment between AI models and humans. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant (No.2019-0-00075, Artificial Intelligence Graduate School Program(KAIST)) and National Research Foundation of Korea (NRF) grant (NRF2020H1D3A2A03100945), funded by the Korea government (MSIT). A.2 Composition What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? VisAlign contains eight different types of images and their corresponding gold human labels. How many instances are there in total (of each type, if appropriate)? There are a total of 12500 images in the train set, distributed equally among the 10 classes. The open test set and the closed test each contain 900 images: 100 images each in Categories 1 to 7 and 200 images in Category 8. Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?

AI alignment refers to models acting towards human-intended goals, preferences, or ethical principles. In this paper, we focus on the models' visual perception alignment with humans, further referred to as AI-human visual alignment. Specifically, we propose a new dataset for measuring AI-human visual alignment in terms of image classification. In order to evaluate AI-human visual alignment, a dataset should encompass samples with various scenarios and have gold human perception labels. Our dataset consists of three groups of samples, namely Must-Act (i.e., Must-Classify), Must-Abstain, and Uncertain, and further divided into eight categories. All samples have a gold human perception label; even Uncertain (e.g., severely blurry) sample labels were obtained via crowd-sourcing. The validity of our dataset is verified by sampling theory, statistical theories related to survey design, and experts in the related fields. Using our dataset, we analyze the visual alignment and reliability of five popular visual perception models and eight abstention methods.

China will ban the sale of drones in Beijing and require permits to fly them under new rules that take effect on Friday. Drones and key components will be prohibited from being sold, rented or brought into the Chinese capital. Drone owners will also be required to register their devices with the police. China has gradually tightened regulations on drones in recent years, with authorities citing public safety concerns. Drones and flying taxis are part of the so-called low-altitude economy, a strategic priority for China that is expected to generate more than two trillion yuan ($290bn; £217bn) by 2035.

The radiology report is the main form of communication between radiologists and other clinicians. Prior work in natural language processing in radiology reports has shown the value of developing methods tailored for individual tasks such as identifying reports with critical results or disease detection. Meanwhile, English and biomedical natural language understanding benchmarks such as the General Language Understanding and Evaluation as well as Biomedical Language Understanding and Reasoning Benchmark have motivated the development of models that can be easily adapted to address many tasks in those domains. Here, we characterize the radiology report as a distinct domain and introduce RaLEs, the Radiology Language Evaluations, as a benchmark for natural language understanding and generation in radiology. RaLEs is comprised of six natural language understanding and generation evaluations including the extraction of anatomical and disease entities and their relations, procedure selection, and report summarization. We characterize the performance of models designed for the general, biomedical, clinical and radiology domains across these tasks. We find that advances in the general and biomedical domains do not necessarily translate to radiology, and that certain more advanced models from the general domain can perform comparably to smaller clinical-specific models. The limited performance of existing pre-trained models on RaLEs highlights the opportunity to improve domain-specific self-supervised models for natural language processing in radiology. We propose RaLEs as a benchmark to promote and track the development of such domain-specific radiology language models.