Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models

Causal graph recovery is essential in the field of causal inference. Traditional methods are typically knowledge-based or statistical estimation-based, which are limited by data collection biases and individuals' knowledge about factors affecting the relations between variables of interests. The advance of large language models (LLMs) provides opportunities to address these problems. We propose a novel method that utilizes the extensive knowledge contained within a large corpus of scientific literature to deduce causal relationships in general causal graph recovery tasks. This method leverages Retrieval Augmented-Generation (RAG) based LLMs to systematically analyze and extract pertinent information from a comprehensive collection of research papers. Our method first retrieves relevant text chunks from the aggregated literature. Then, the LLM is tasked with identifying and labelling potential associations between factors. Finally, we give a method to aggregate the associational relationships to build a causal graph. We demonstrate our method is able to construct high quality causal graphs on the well-known SACHS dataset solely from literature.

VideoDreamer: Customized Multi-Subject Text-to-Video Generation with Disen-Mix Finetuning

Customized text-to-video generation aims to generate text-guided videos with customized user-given subjects, which has gained increasing attention recently. However, existing works are primarily limited to generating videos for a single subject, leaving the more challenging problem of customized multi-subject text-to-video generation largely unexplored. In this paper, we fill this gap and propose a novel VideoDreamer framework. VideoDreamer can generate temporally consistent text-guided videos that faithfully preserve the visual features of the given multiple subjects. Specifically, VideoDreamer leverages the pretrained Stable Diffusion with latent-code motion dynamics and temporal cross-frame attention as the base video generator. The video generator is further customized for the given multiple subjects by the proposed Disen-Mix Finetuning and Human-in-the-Loop Re-finetuning strategy, which can tackle the attribute binding problem of multi-subject generation. We also introduce MultiStudioBench, a benchmark for evaluating customized multi-subject text-to-video generation models. Extensive experiments demonstrate the remarkable ability of VideoDreamer to generate videos with new content such as new events and backgrounds, tailored to the customized multiple subjects. Our project page is available at https://videodreamer23.github.io/.

DisenBooth: Disentangled Parameter-Efficient Tuning for Subject-Driven Text-to-Image Generation

Given a small set of images of a specific subject, subject-driven text-to-image generation aims to generate customized images of the subject according to new text descriptions, which has attracted increasing attention in the community recently. Current subject-driven text-to-image generation methods are mainly based on finetuning a pretrained large-scale text-to-image generation model. However, these finetuning methods map the images of the subject into an embedding highly entangled with subject-identity-unrelated information, which may result in the inconsistency between the generated images and the text descriptions and the changes in the subject identity. To tackle the problem, we propose DisenBooth, a disentangled parameter-efficient tuning framework for subject-driven text-to-image generation. DisenBooth enables generating new images that simultaneously preserve the subject identity and conform to the text descriptions, by disentangling the embedding into an identity-related and an identity-unrelated part. Specifically, DisenBooth is based on the pretrained diffusion models and conducts finetuning in the diffusion denoising process, where a shared identity embedding and an image-specific identity-unrelated embedding are utilized jointly for denoising each image. To make the two embeddings disentangled, two auxiliary objectives are proposed. Additionally, to improve the finetuning efficiency, a parameter-efficient finetuning strategy is adopted. Extensive experiments show that our DisenBooth can faithfully learn well-disentangled identity-related and identity-unrelated embeddings. With the shared identity embedding, DisenBooth demonstrates superior subject-driven text-to-image generation ability. Additionally, DisenBooth provides a more flexible and controllable framework with different combinations of the disentangled embeddings.

On the Security Vulnerabilities of Text-to-SQL Models

Recent studies show that, despite being effective on numerous tasks, text processing algorithms may be vulnerable to deliberate attacks. However, the question of whether such weaknesses can directly lead to security threats is still under-explored. To bridge this gap, we conducted vulnerability tests on Text-to-SQL, a technique that builds natural language interfaces for databases. Empirically, we showed that the Text-to-SQL modules of two commercial black boxes (Baidu-UNIT and Codex-powered Ai2sql) can be manipulated to produce malicious code, potentially leading to data breaches and Denial of Service. This is the first demonstration of the danger of NLP models being exploited as attack vectors in the wild. Moreover, experiments involving four open-source frameworks verified that simple backdoor attacks can achieve a 100% success rate on Text-to-SQL systems with almost no prediction performance impact. By reporting these findings and suggesting practical defences, we call for immediate attention from the NLP community to the identification and remediation of software security issues.

Action-conditioned On-demand Motion Generation

We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows improvements over SOTA approaches on all traditional motion evaluation metrics when evaluated on three public datasets (HumanAct12, UESTC, and MoCap). Furthermore, we provide both qualitative evaluations and quantitative metrics demonstrating several first-known customization capabilities afforded by our framework, including mode discovery, interpolation, and trajectory customization. These capabilities significantly widen the spectrum of potential applications of such motion generation models. The novel on-demand generative capabilities are enabled by innovations in both the encoder and decoder architectures: (i) Encoder: Utilizing contrastive learning in low-dimensional latent space to create a hierarchical embedding of motion sequences, where not only the codes of different action types form different groups, but within an action type, codes of similar inherent patterns (motion styles) cluster together, making them readily discoverable; (ii) Decoder: Using a hierarchical decoding strategy where the motion trajectory is reconstructed first and then used to reconstruct the whole motion sequence. Such an architecture enables effective trajectory control. Our code is released on the Github page: https://github.com/roychowdhuryresearch/ODMO

Fine-Grained Visual Entailment

Visual entailment is a recently proposed multimodal reasoning task where the goal is to predict the logical relationship of a piece of text to an image. In this paper, we propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image. Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity. Because we lack fine-grained labels to train our method, we propose a novel multi-instance learning approach which learns a fine-grained labeling using only sample-level supervision. We also impose novel semantic structural constraints which ensure that fine-grained predictions are internally semantically consistent. We evaluate our method on a new dataset of manually annotated knowledge elements and show that our method achieves 68.18\% accuracy at this challenging task while significantly outperforming several strong baselines. Finally, we present extensive qualitative results illustrating our method's predictions and the visual evidence our method relied on. Our code and annotated dataset can be found here: https://github.com/SkrighYZ/FGVE.

Delving into Rectifiers in Style-Based Image Translation

While modern image translation techniques can create photorealistic synthetic images, they have limited style controllability, thus could suffer from translation errors. In this work, we show that the activation function is one of the crucial components in controlling the direction of image synthesis. Specifically, we explicitly demonstrated that the slope parameters of the rectifier could change the data distribution and be used independently to control the direction of translation. To improve the style controllability, two simple but effective techniques are proposed, including Adaptive ReLU (AdaReLU) and structural adaptive function. The AdaReLU can dynamically adjust the slope parameters according to the target style and can be utilized to increase the controllability by combining with Adaptive Instance Normalization (AdaIN). Meanwhile, the structural adaptative function enables rectifiers to manipulate the structure of feature maps more effectively. It is composed of the proposed structural convolution (StruConv), an efficient convolutional module that can choose the area to be activated based on the mean and variance specified by AdaIN. Extensive experiments show that the proposed techniques can greatly increase the network controllability and output diversity in style-based image translation tasks.

Conditional Sequential Slate Optimization

The top search results matching a user query that are displayed on the first page are critical to the effectiveness and perception of a search system. A search ranking system typically orders the results by independent query-document scores to produce a slate of search results. However, such unilateral scoring methods may fail to capture inter-document dependencies that users are sensitive to, thus producing a sub-optimal slate. Further, in practice, many real-world applications such as e-commerce search require enforcing certain distributional criteria at the slate-level, due to business objectives or long term user retention goals. Unilateral scoring of results does not explicitly support optimizing for such objectives with respect to a slate. Hence, solutions to the slate optimization problem must consider the optimal selection and order of the documents, along with adherence to slate-level distributional criteria. To that end, we propose a hybrid framework extended from traditional slate optimization to solve the conditional slate optimization problem. We introduce conditional sequential slate optimization (CSSO), which jointly learns to optimize for traditional ranking metrics as well as prescribed distribution criteria of documents within the slate. The proposed method can be applied to practical real world problems such as enforcing diversity in e-commerce search results, mitigating bias in top results and personalization of results. Experiments on public datasets and real-world data from e-commerce datasets show that CSSO outperforms popular comparable ranking methods in terms of adherence to distributional criteria while producing comparable or better relevance metrics.