RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text

The fixed-size context of Transformer makes GPT models incapable of generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a language-based simulacrum of the recurrence mechanism in RNNs. RecurrentGPT is built upon a large language model (LLM) such as ChatGPT and uses natural language to simulate the Long Short-Term Memory mechanism in an LSTM. At each timestep, RecurrentGPT generates a paragraph of text and updates its language-based long-short term memory stored on the hard drive and the prompt, respectively. This recurrence mechanism enables RecurrentGPT to generate texts of arbitrary length without forgetting. Since human users can easily observe and edit the natural language memories, RecurrentGPT is interpretable and enables interactive generation of long text. RecurrentGPT is an initial step towards next-generation computer-assisted writing systems beyond local editing suggestions. In addition to producing AI-generated content (AIGC), we also demonstrate the possibility of using RecurrentGPT as an interactive fiction that directly interacts with consumers. We call this usage of generative models by ``AI As Contents'' (AIAC), which we believe is the next form of conventional AIGC. We further demonstrate the possibility of using RecurrentGPT to create personalized interactive fiction that directly interacts with readers instead of interacting with writers. More broadly, RecurrentGPT demonstrates the utility of borrowing ideas from popular model designs in cognitive science and deep learning for prompting LLMs. Our code is available at https://github.com/aiwaves-cn/RecurrentGPT and an online demo is available at https://www.aiwaves.org/recurrentgpt.

BOSH: An Efficient Meta Algorithm for Decision-based Attacks

Adversarial example generation becomes a viable method for evaluating the robustness of a machine learning model. In this paper, we consider hard-label black-box attacks (a.k.a. decision-based attacks), which is a challenging setting that generates adversarial examples based on only a series of black-box hard-label queries. This type of attacks can be used to attack discrete and complex models, such as Gradient Boosting Decision Tree (GBDT) and detection-based defense models. Existing decision-based attacks based on iterative local updates often get stuck in a local minimum and fail to generate the optimal adversarial example with the smallest distortion. To remedy this issue, we propose an efficient meta algorithm called BOSH-attack, which tremendously improves existing algorithms through Bayesian Optimization (BO) and Successive Halving (SH). In particular, instead of traversing a single solution path when searching an adversarial example, we maintain a pool of solution paths to explore important regions. We show empirically that the proposed algorithm converges to a better solution than existing approaches, while the query count is smaller than applying multiple random initializations by a factor of 10.

Toward Finding The Global Optimal of Adversarial Examples

Current machine learning models are vulnerable to adversarial examples (Goodfellow et al., 2014), we noticed that current state-of-the-art methods (Kurakin et al., 2016; Cheng et al., 2018) to attack a well-trained model often stuck in local optimal values. We conduct series of experiments on both white-box and black-box settings, and find out that by different initialization, the attack algorithm will finally converge to very different local optimals, suggesting the importance of careful and thorough search in the attack space. In this paper, we propose a general boosting algorithm that can help current attack to find a more global optimal example. Specifically, we search for the adversarial examples by starting from different points/directions, and in certain interval we adopt successive halving (Jamieson & Talwalkar, 2016) to cut down the searching directions that are not promising, and use Bayesian Optimization (Pelikan et al., 1999; Bergstra et al., 2011) to resample from the search space based on the knowledge obtained from past searches. We demonstrate that by applying our methods to state-of-the-art attack algorithms in both black-and white box setting, we can further reduce the distortion between the original image and adversarial sample about 10%-20%. By adopting dynamic successive halving, we can reduce the computation cost 5-10 times without harming the final result. We conduct experiments in models trained on MNIST or ImageNet and also try on decision tree models, these experiments suggest that our method is a general way to boost the performance of current adversarial attack methods.

Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos

Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, video context modeling or multi-modal fusion, thus fail to develop a comprehensive system for further performance improvement. In this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to consider multiple crucial factors for this challenging task, including (1) the syntactic structure of natural language queries; (2) long-range semantic dependencies in video context and (3) the sufficient cross-modal interaction. Specifically, we devise a syntactic GCN to leverage the syntactic structure of queries for fine-grained representation learning, propose a multi-head self-attention to capture long-range semantic dependencies from video context, and next employ a multi-stage cross-modal interaction to explore the potential relations of video and query contents. The extensive experiments demonstrate the effectiveness of our proposed method.