Even though trained mainly on images, we discover that pretrained diffusion models show impressive power in guiding sketch synthesis. In this paper, we present DiffSketcher, an innovative algorithm that creates \textit{vectorized} free-hand sketches using natural language input. DiffSketcher is developed based on a pre-trained text-to-image diffusion model. It performs the task by directly optimizing a set of B\'ezier curves with an extended version of the score distillation sampling (SDS) loss, which allows us to use a raster-level diffusion model as a prior for optimizing a parametric vectorized sketch generator. Furthermore, we explore attention maps embedded in the diffusion model for effective stroke initialization to speed up the generation process. The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual details of the subject drawn. Our experiments show that DiffSketcher achieves greater quality than prior work. The code and demo of DiffSketcher can be found at https://ximinng.github.io/DiffSketcher-project/.

George Carlin's family is calling out an AI-generated comedy special using the late comedian's voice and style. Dudesy, a comedy AI platform founded by "Mad TV" alum Will Sasso and author and screenwriter Chad Kultgen, released an hourlong comedy special titled "George Carlin: I'm Glad I'm Dead," on YouTube and other platforms. In the introduction, a robotic voice declares, "I'm Dudesy, and I'm a comedy AI. What you're about to hear is my second hourlong special. Before I get started, I just want to let you know very clearly that what you're about to hear is not George Carlin. It's my impression of George Carlin that I developed in the exact same way a human impressionist would."

Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations. We introduce Truth Forest, a method that enhances truthfulness in LLMs by uncovering hidden truth representations using multi-dimensional orthogonal probes. Specifically, it creates multiple orthogonal bases for modeling truth by incorporating orthogonal constraints into the probes. Moreover, we introduce Random Peek, a systematic technique considering an extended range of positions within the sequence, reducing the gap between discerning and generating truth features in LLMs. By employing this approach, we improved the truthfulness of Llama-2-7B from 40.8\% to 74.5\% on TruthfulQA. Likewise, significant improvements are observed in fine-tuned models. We conducted a thorough analysis of truth features using probes. Our visualization results show that orthogonal probes capture complementary truth-related features, forming well-defined clusters that reveal the inherent structure of the dataset.

As AI systems quickly improve in both breadth and depth of performance, they lend themselves to creating increasingly powerful and realistic agents, including the possibility of agents modeled on specific people. We anticipate that within our lifetimes it may become common practice for people to create a custom AI agent to interact with loved ones and/or the broader world after death. We call these generative ghosts, since such agents will be capable of generating novel content rather than merely parroting content produced by their creator while living. In this paper, we first discuss the design space of potential implementations of generative ghosts. We then discuss the practical and ethical implications of generative ghosts, including potential positive and negative impacts on individuals and society. Based on these considerations, we lay out a research agenda for the AI and HCI research communities to empower people to create and interact with AI afterlives in a safe and beneficial manner.

Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences. However, a major challenge arises from the sparsity of these reward signals - typically, there is only one reward for the entire generation. This sparsity of rewards can lead to inefficient and unstable learning. In this paper, we introduce a novel framework leveraging the critique ability of LLMs to produce dense rewards throughout the learning process. Our approach incorporates a critic language model alongside the policy model. This critic is prompted with the task description, question, policy model's output, and environment's reward signal as input, and provides token or span-level dense rewards that reflect the quality of each segment of the output. We assess our approach on three text generation tasks: sentiment control, language model detoxification, and summarization. Experimental results show that incorporating artificial dense rewards in training yields consistent performance gains over the PPO baseline with holistic rewards. Furthermore, in a setting where the same model serves as both policy and critic, we demonstrate that "self-critique" rewards also boost learning efficiency.

The use of propagandistic techniques in online contents has increased in recent years aiming to manipulate online audiences. Efforts to automatically detect and debunk such content have been made addressing various modeling scenarios. These include determining whether the content (text, image, or multimodal) (i) is propagandistic, (ii) employs one or more propagandistic techniques, and (iii) includes techniques with identifiable spans. Significant research efforts have been devoted to the first two scenarios compared to the latter. Therefore, in this study, we focus on the task of detecting propagandistic textual spans. Specifically, we investigate whether large language models (LLMs), such as GPT-4, can effectively perform the task. Moreover, we study the potential of employing the model to collect more cost-effective annotations. Our experiments use a large-scale in-house dataset consisting of annotations from human annotators with varying expertise levels. The results suggest that providing more information to the model as prompts improves its performance compared to human annotations. Moreover, our work is the first to show the potential of utilizing LLMs to develop annotated datasets for this specific task, prompting it with annotations from human annotators with limited expertise. We plan to make the collected span-level labels from multiple annotators, including GPT-4, available for the community.

Text-based knowledge graph completion (KGC) methods, leveraging textual entity descriptions are at the research forefront. The efficacy of these models hinges on the quality of the textual data. This study explores whether enriched or more efficient textual descriptions can amplify model performance. Recently, Large Language Models (LLMs) have shown remarkable improvements in NLP tasks, attributed to their sophisticated text generation and conversational capabilities. LLMs assimilate linguistic patterns and integrate knowledge from their training data. Compared to traditional databases like Wikipedia, LLMs provide several advantages, facilitating broader information querying and content augmentation. We hypothesize that LLMs, without fine-tuning, can refine entity descriptions, serving as an auxiliary knowledge source. An in-depth analysis was conducted to verify this hypothesis. We found that (1) without fine-tuning, LLMs have the capability to further improve the quality of entity text descriptions. We validated this through experiments on the FB15K-237 and WN18RR datasets. (2) LLMs exhibit text generation hallucination issues and selectively output words with multiple meanings. This was mitigated by contextualizing prompts to constrain LLM outputs. (3) Larger model sizes do not necessarily guarantee better performance; even the 7B model can achieve optimized results in this comparative task. These findings underscore the untapped potential of large models in text-based KGC, which is a promising direction for further research in KGC. The code and datasets are accessible at \href{https://github.com/sjlmg/CP-KGC}.

Hypershell ProX is like a second skin that fits over your legs and boosts strength, speed and endurance. Do you love exploring the great outdoors, but feel limited by your physical stamina or the weight of your backpack? Do you wish you could run faster, hike longer and trek farther without getting tired or sore? If you answered yes, then you're in luck. Hypershell, a robot startup from Y-Combinator China, has created the Hypershell ProX, an all-terrain exoskeleton that will take your outdoor adventures to the next level.

Vector databases have emerged as key enablers for bridging intelligent applications with unstructured data, providing generic search and management support for embedding vectors extracted from the raw unstructured data. As multiple data users can share the same database infrastructure, multi-tenancy support for vector databases is increasingly desirable. This hinges on an efficient filtered search operation, i.e., only querying the vectors accessible to a particular tenant. Multi-tenancy in vector databases is currently achieved by building either a single, shared index among all tenants, or a per-tenant index. The former optimizes for memory efficiency at the expense of search performance, while the latter does the opposite. Instead, this paper presents Curator, an in-memory vector index design tailored for multi-tenant queries that simultaneously achieves the two conflicting goals, low memory overhead and high performance for queries, vector insertion, and deletion. Curator indexes each tenant's vectors with a tenant-specific clustering tree and encodes these trees compactly as sub-trees of a shared clustering tree. Each tenant's clustering tree adapts dynamically to its unique vector distribution, while maintaining a low per-tenant memory footprint. Our evaluation, based on two widely used data sets, confirms that Curator delivers search performance on par with per-tenant indexing, while maintaining memory consumption at the same level as metadata filtering on a single, shared index.

Recently, many studies utilized adversarial examples (AEs) to raise the cost of malicious image editing and copyright violation powered by latent diffusion models (LDMs). Despite their successes, a few have studied the surrogate model they used to generate AEs. In this paper, from the perspective of adversarial transferability, we investigate how the surrogate model's property influences the performance of AEs for LDMs. Specifically, we view the time-step sampling in the Monte-Carlo-based (MC-based) adversarial attack as selecting surrogate models. We find that the smoothness of surrogate models at different time steps differs, and we substantially improve the performance of the MC-based AEs by selecting smoother surrogate models. In the light of the theoretical framework on adversarial transferability in image classification, we also conduct a theoretical analysis to explain why smooth surrogate models can also boost AEs for LDMs.