We investigate the application of Retrieval-Augmented Generation (RAG) for enhancing the analysis of corporate sustainability disclosures. We introduce CorSus, a novel dataset for evaluating RAG models in answering corporate sustainability-focused claims, using data from the Transition Pathway Initiative for over 100 companies. Further, we develop a subset of this dataset with reference documentation and fully explained answers. Finally, in a systematic framework, we optimise and benchmark state-of-the-art RAG approaches using the CorSus dataset. With this work, we aim to empower stakeholders with a tool for informed evaluations of corporate sustainability practices, thereby encouraging a greater commitment to environmental responsibility.

In recent years, there has been a great deal of studies about the optimisation of generating adversarial examples for Deep Neural Networks (DNNs) in a black-box environment. The use of gradient-based techniques to get the adversarial images in a minimal amount of input-output correspondence with the attacked model has been extensively studied. However, existing studies have not been discussing the effect of different gradient estimation techniques coherently. In this paper, a new one-point residual estimate is compared to the known two-point estimates. The findings in this paper show that the one-point residual estimate is not a viable option to decrease the number of queries to the attacked model. The accuracy of the attacks with the use of an one-point residual estimate maintains the same for weaker models. For stronger models, there is a slight decrease in accuracy at identical distortion levels. All estimates are tested on different PGD attacks on the MNIST and F-MNIST datasets using a 3-layer convolutional network.