In today's machine learning (ML) models, any part of the training data can affect its output. This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data. To enable secure machine learning for access controlled data, we propose the notion of information flow control for machine learning, and develop a secure Transformer-based language model based on the Mixture-of-Experts (MoE) architecture. The secure MoE architecture controls information flow by limiting the influence of training data from each security domain to a single expert module, and only enabling a subset of experts at inference time based on an access control policy. The evaluation using a large corpus of text data shows that the proposed MoE architecture has minimal (1.9%) performance overhead and can significantly improve model accuracy (up to 37%) by enabling training on access-controlled data.
With the increasing diversity of use cases of large language models, a more informative treatment of texts seems necessary. An argumentative analysis could foster a more reasoned usage of chatbots, text completion mechanisms or other applications. However, it is unclear which aspects of argumentation can be reliably identified and integrated in language models. In this paper, we present an empirical assessment of the reliability with which different argumentative aspects can be automatically identified in hate speech in social media. We have enriched the Hateval corpus (Basile et al. 2019) with a manual annotation of some argumentative components, adapted from Wagemans (2016)'s Periodic Table of Arguments. We show that some components can be identified with reasonable reliability. For those that present a high error ratio, we analyze the patterns of disagreement between expert annotators and errors in automatic procedures, and we propose adaptations of those categories that can be more reliably reproduced.
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos. Despite their success, there has been limited research on how the underlying diffusion process and the final convergent prior can affect generative performance; this research has also been limited to continuous data types and a score-based diffusion framework. To fill this gap, we explore how different discrete diffusion kernels (which converge to different prior distributions) affect the performance of diffusion models for graphs. To this end, we developed a novel formulation of a family of discrete diffusion kernels which are easily adjustable to converge to different Bernoulli priors, and we study the effect of these different kernels on generative performance. We show that the quality of generated graphs is sensitive to the prior used, and that the optimal choice cannot be explained by obvious statistics or metrics, which challenges the intuitions which previous works have suggested.
Can a Large Language Model (LLM) solve simple abstract reasoning problems? We explore this broad question through a systematic analysis of GPT on the Abstraction and Reasoning Corpus (ARC), a representative benchmark of abstract reasoning ability from limited examples in which solutions require some "core knowledge" of concepts such as objects, goal states, counting, and basic geometry. GPT-4 solves only 13/50 of the most straightforward ARC tasks when using textual encodings for their two-dimensional input-output grids. Our failure analysis reveals that GPT-4's capacity to identify objects and reason about them is significantly influenced by the sequential nature of the text that represents an object within a text encoding of a task. To test this hypothesis, we design a new benchmark, the 1D-ARC, which consists of one-dimensional (array-like) tasks that are more conducive to GPT-based reasoning, and where it indeed performs better than on the (2D) ARC. To alleviate this issue, we propose an object-based representation that is obtained through an external tool, resulting in nearly doubling the performance on solved ARC tasks and near-perfect scores on the easier 1D-ARC. Although the state-of-the-art GPT-4 is unable to "reason" perfectly within non-language domains such as the 1D-ARC or a simple ARC subset, our study reveals that the use of object-based representations can significantly improve its reasoning ability. Visualizations, GPT logs, and data are available at https://khalil-research.github.io/LLM4ARC.
State-of-the-art video-text retrieval (VTR) methods usually fully fine-tune the pre-trained model (e.g. CLIP) on specific datasets, which may suffer from substantial storage costs in practical applications since a separate model per task needs to be stored. To overcome this issue, we present the premier work on performing parameter-efficient VTR from the pre-trained model, i.e., only a small number of parameters are tunable while freezing the backbone. Towards this goal, we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically, MV-Adapter adopts bottleneck structures in both video and text branches and introduces two novel components. The first is a Temporal Adaptation Module employed in the video branch to inject global and local temporal contexts. We also learn weights calibrations to adapt to the dynamic variations across frames. The second is a Cross-Modal Interaction Module that generates weights for video/text branches through a shared parameter space, for better aligning between modalities. Thanks to above innovations, MV-Adapter can achieve on-par or better performance than standard fine-tuning with negligible parameters overhead. Notably, on five widely used VTR benchmarks (MSR-VTT, MSVD, LSMDC, DiDemo, and ActivityNet), MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins. Codes will be released.
Contrastive learning has emerged as an efficient framework to learn multimodal representations. CLIP, a seminal work in this area, achieved impressive results by training on paired image-text data using the contrastive loss. Recent work claims improvements over CLIP using additional non-contrastive losses inspired from self-supervised learning. However, it is sometimes hard to disentangle the contribution of these additional losses from other implementation details, e.g., data augmentation or regularization techniques, used to train the model. To shed light on this matter, in this paper, we first propose, implement and evaluate several baselines obtained by combining contrastive learning with recent advances in self-supervised learning. In particular, we use the loss functions that were proven successful for visual self-supervised learning to align image and text modalities. We find that these baselines outperform a basic implementation of CLIP. However, when a stronger training recipe is employed, the advantage disappears. Indeed, we find that a simple CLIP baseline can also be improved substantially, up to a 25% relative improvement on downstream zero-shot tasks, by using well-known training techniques that are popular in other subfields. Moreover, we discover that it is enough to apply image and text augmentations to make up for most of the improvement attained by prior works. With our improved training recipe for CLIP, we obtain state-of-the-art performance on four standard datasets, and consistently outperform prior work (up to +4% on the largest dataset), while being substantially simpler.
We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we ``carve'' the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety.
Large language models (LLMs) have recently soared in popularity due to their ease of access and the unprecedented intelligence exhibited on diverse applications. However, LLMs like ChatGPT present significant limitations in supporting complex information tasks due to the insufficient affordances of the text-based medium and linear conversational structure. Through a formative study with ten participants, we found that LLM interfaces often present long-winded responses, making it difficult for people to quickly comprehend and interact flexibly with various pieces of information, particularly during more complex tasks. We present Graphologue, an interactive system that converts text-based responses from LLMs into graphical diagrams to facilitate information-seeking and question-answering tasks. Graphologue employs novel prompting strategies and interface designs to extract entities and relationships from LLM responses and constructs node-link diagrams in real-time. Further, users can interact with the diagrams to flexibly adjust the graphical presentation and to submit context-specific prompts to obtain more information. Utilizing diagrams, Graphologue enables graphical, non-linear dialogues between humans and LLMs, facilitating information exploration, organization, and comprehension.
Recently, a large number of studies have shown that the introduction of visual information can effectively improve the effect of neural machine translation (NMT). Its effectiveness largely depends on the availability of a large number of bilingual parallel sentence pairs and manual image annotation. The lack of images and the effectiveness of images have been difficult to solve. In this paper, a multimodal pre-training generalization algorithm for self-supervised training is proposed, which overcomes the lack of visual information and inaccuracy, and thus extends the applicability of images on NMT. Specifically, we will search for many pictures from the existing sentences through the search engine, and then through the relationship between visual information and text, do the self-supervised training task of graphics and text to obtain more effective visual information for text. We show that when the filtered information is used as multimodal machine translation for fine-tuning, the effect of translation in the global voice dataset is 0.5 BLEU higher than the baseline.
Diffusion models have emerged as a powerful method of generative modeling across a range of fields, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack explicit control over the 3D structure of the objects in the generated images. In this paper, we propose a novel method that incorporates 3D geometry control into diffusion models, making them generate even more realistic and diverse images. To achieve this, our method exploits ControlNet, which extends diffusion models by using visual prompts in addition to text prompts. We generate images of 3D objects taken from a 3D shape repository (e.g., ShapeNet and Objaverse), render them from a variety of poses and viewing directions, compute the edge maps of the rendered images, and use these edge maps as visual prompts to generate realistic images. With explicit 3D geometry control, we can easily change the 3D structures of the objects in the generated images and obtain ground-truth 3D annotations automatically. This allows us to use the generated images to improve a lot of vision tasks, e.g., classification and 3D pose estimation, in both in-distribution (ID) and out-of-distribution (OOD) settings. We demonstrate the effectiveness of our method through extensive experiments on ImageNet-50, ImageNet-R, PASCAL3D+, ObjectNet3D, and OOD-CV datasets. The results show that our method significantly outperforms existing methods across multiple benchmarks (e.g., 4.6 percentage points on ImageNet-50 using ViT and 3.5 percentage points on PASCAL3D+ and ObjectNet3D using NeMo).