arXiv subjects

Vineeth Ravi

Publications and source records attributed to Vineeth Ravi.

4 recordsLinked to original sources

arXiv2024

IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videos

Shape assembly is a ubiquitous task in daily life, integral for constructing complex 3D structures like IKEA furniture. While significant progress has been made in developing autonomous agents for shape assembly, existing datasets have not yet tackled the 4D grounding of assembly instructions in videos, essential for a holistic understanding of assembly in 3D space over time. We introduce IKEA Video Manuals, a dataset that features 3D models of furniture parts, instructional manuals, assembly videos from the Internet, and most importantly, annotations of dense spatio-temporal alignments between these data modalities. To demonstrate the utility of IKEA Video Manuals, we present five applications essential for shape assembly: assembly plan generation, part-conditioned segmentation, part-conditioned pose estimation, video object segmentation, and furniture assembly based on instructional video manuals. For each application, we provide evaluation metrics and baseline methods. Through experiments on our annotated data, we highlight many challenges in grounding assembly instructions in videos to improve shape assembly, including handling occlusions, varying viewpoints, and extended assembly sequences.

cs.CV↗

arXiv2024

Can LLMs be Scammed? A Baseline Measurement Study

Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to such threats. In this work, we address this gap by constructing a benchmark based on the FINRA taxonomy and systematically assessing Large Language Models' (LLMs') vulnerability to a variety of scam tactics. First, we incorporate 37 well-defined base scam scenarios reflecting the diverse scam categories identified by FINRA taxonomy, providing a focused evaluation of LLMs' scam detection capabilities. Second, we utilize representative proprietary (GPT-3.5, GPT-4) and open-source (Llama) models to analyze their performance in scam detection. Third, our research provides critical insights into which scam tactics are most effective against LLMs and how varying persona traits and persuasive techniques influence these vulnerabilities. We reveal distinct susceptibility patterns across different models and scenarios, underscoring the need for targeted enhancements in LLM design and deployment.

cs.CR↗

arXiv2024

Shortchanged: Uncovering and Analyzing Intimate Partner Financial Abuse in Consumer Complaints

Digital financial services can introduce new digital-safety risks for users, particularly survivors of intimate partner financial abuse (IPFA). To offer improved support for such users, a comprehensive understanding of their support needs and the barriers they face to redress by financial institutions is essential. Drawing from a dataset of 2.7 million customer complaints, we implement a bespoke workflow that utilizes language-modeling techniques and expert human review to identify complaints describing IPFA. Our mixed-method analysis provides insight into the most common digital financial products involved in these attacks, and the barriers consumers report encountering when doing so. Our contributions are twofold; we offer the first human-labeled dataset for this overlooked harm and provide practical implications for technical practice, research, and design for better supporting and protecting survivors of IPFA.

cs.CY↗

arXiv2021

DocuBot : Generating financial reports using natural language interactions

The financial services industry perpetually processes an overwhelming amount of complex data. Digital reports are often created based on tedious manual analysis as well as visualization of the underlying trends and characteristics of data. Often, the accruing costs of human computation errors in creating these reports are very high. We present DocuBot, a novel AI-powered virtual assistant for creating and modifying content in digital documents by modeling natural language interactions as "skills" and using them to transform underlying data. DocuBot has the ability to agglomerate saved skills for reuse, enabling humans to automatically generate recurrent reports. DocuBot also has the capability to continuously learn domain-specific and user-specific vocabulary by interacting with the user. We present evidence that DocuBot adds value to the financial industry and demonstrate its impact with experiments involving real and simulated users tasked with creating PowerPoint presentations.

cs.CL↗