arXiv subjects

Chinmay Kulkarni

Publications and source records attributed to Chinmay Kulkarni.

15 recordsLinked to original sources

arXiv2025

Multi Agent based Medical Assistant for Edge Devices

Large Action Models (LAMs) have revolutionized intelligent automation, but their application in healthcare faces challenges due to privacy concerns, latency, and dependency on internet access. This report introduces an ondevice, multi-agent healthcare assistant that overcomes these limitations. The system utilizes smaller, task-specific agents to optimize resources, ensure scalability and high performance. Our proposed system acts as a one-stop solution for health care needs with features like appointment booking, health monitoring, medication reminders, and daily health reporting. Powered by the Qwen Code Instruct 2.5 7B model, the Planner and Caller Agents achieve an average RougeL score of 85.5 for planning and 96.5 for calling for our tasks while being lightweight for on-device deployment. This innovative approach combines the benefits of ondevice systems with multi-agent architectures, paving the way for user-centric healthcare solutions.

cs.MA↗

arXiv2025

Model Behavior Specification by Leveraging LLM Self-Playing and Self-Improving

Training AI models is challenging, particularly when crafting behavior instructions. Traditional methods rely on machines (supervised learning) or manual pattern discovery, which results in not interpretable models or time sink. While Large Language Models (LLMs) simplify instruction writing through natural language, articulating intended model behavior still remains difficult. We introduce Visionary Tuning, a human-in-the-loop self-playing followed by automatic self-refinement to improve behavior specification. Our system helps users clarify desired behavior through self-playing and generates prompts through self-improving, Our first evaluation involves user study conducted on a system implementation of Visionary Tuning within the context of chatbot behavior. Our system self-play itself by simulating user interactions to identify patterns and create effective prompts based on the pattern. In a within-subject study (N=12), participants pinpointed more patterns through self-playing and crafted better prompts. Surprisingly, users felt more or less success level in specifying the model behavior. Follow-up crowd studies (N=60) confirmed that the chatbot adhered to instructions without sacrificing quality. Our second evaluation is a case study on a real-world implementation using a movie rating dataset with Visionary Tuning, demonstrating its effectiveness and robustness in modeling a critic's preferences across the spectrum of low to highly rated movies. Together, these results suggest how AI improves the design process of interactive AI systems. Furthermore, they suggest how the benefits of these tools may be non-obvious to end-users. We reflect on these findings and suggest future directions.

cs.HC↗

arXiv2024

Thinking Assistants: LLM-Based Conversational Assistants that Help Users Think By Asking rather than Answering

Many AI systems focus solely on providing solutions or explaining outcomes. However, complex tasks like research and strategic thinking often benefit from a more comprehensive approach to augmenting the thinking process rather than passively getting information. We introduce the concept of "Thinking Assistant", a new genre of assistants that help users improve decision-making with a combination of asking reflection questions based on expert knowledge. Through our lab study (N=80), these Large Language Model (LLM) based Thinking Assistants were better able to guide users to make important decisions, compared with conversational agents that only asked questions, provided advice, or neither. Based on the results, we develop a Thinking Assistant in academic career development, determining research trajectory or developing one's unique research identity, which requires deliberation, reflection and experts' advice accordingly. In a longitudinal deployment with 223 conversations, participants responded positively to approximately 65% of the responses. Our work proposes directions for developing more effective LLM agents. Rather than adhering to the prevailing authoritative approach of generating definitive answers, LLM agents aimed at assisting with cognitive enhancement should prioritize fostering reflection. They should initially provide responses designed to prompt thoughtful consideration through inquiring, followed by offering advice only after gaining a deeper understanding of the user's context and needs.

cs.HC↗

arXiv2024

Hug Reports: Supporting Expression of Appreciation between Users and Contributors of Open Source Software Packages

Contributors to open source software packages often describe feeling discouraged by the lack of positive feedback from users. This paper describes a technology probe, Hug Reports, that provides users a communication affordance within their code editors, through which users can convey appreciation to contributors of packages they use. In our field study, 18 users interacted with the probe for 3 weeks, resulting in messages of appreciation to 550 contributors, 26 of whom participated in subsequent research. Our findings show how locating a communication affordance within the code editor, and allowing users to express appreciation in terms of the abstractions they are exposed to (packages, modules, functions), can support exchanges of appreciation that are meaningful to users and contributors. Findings also revealed the moments in which users expressed appreciation, the two meanings that appreciation took on -- as a measure of utility and as an act of expressive communication -- and how contributors' reactions to appreciation were influenced by their perceived level of contribution. Based on these findings, we discuss opportunities and challenges for designing appreciation systems for open source in particular, and peer production communities more generally.

cs.HC↗

arXiv2024

Interactive AI Alignment: Specification, Process, and Evaluation Alignment

Modern AI enables a high-level, declarative form of interaction: Users describe the intended outcome they wish an AI to produce, but do not actually create the outcome themselves. In contrast, in traditional user interfaces, users invoke specific operations to create the desired outcome. This paper revisits the basic input-output interaction cycle in light of this declarative style of interaction, and connects concepts in AI alignment to define three objectives for interactive alignment of AI: specification alignment (aligning on what to do), process alignment (aligning on how to do it), and evaluation alignment (assisting users in verifying and understanding what was produced). Using existing systems as examples, we show how these user-centered views of AI alignment can be used descriptively, prescriptively, and as an evaluative aid.

cs.HC↗

arXiv2024

Farsight: Fostering Responsible AI Awareness During AI Application Prototyping

Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping. Based on a user's prompt, Farsight highlights news articles about relevant AI incidents and allows users to explore and edit LLM-generated use cases, stakeholders, and harms. We report design insights from a co-design study with 10 AI prototypers and findings from a user study with 42 AI prototypers. After using Farsight, AI prototypers in our user study are better able to independently identify potential harms associated with a prompt and find our tool more useful and usable than existing resources. Their qualitative feedback also highlights that Farsight encourages them to focus on end-users and think beyond immediate harms. We discuss these findings and reflect on their implications for designing AI prototyping experiences that meaningfully engage with AI harms. Farsight is publicly accessible at: https://PAIR-code.github.io/farsight.

cs.HC↗

arXiv2023

Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses

Large language model (LLM) powered chatbots are primarily text-based today, and impose a large interactional cognitive load, especially for exploratory or sensemaking tasks such as planning a trip or learning about a new city. Because the interaction is textual, users have little scaffolding in the way of structure, informational "scent", or ability to specify high-level preferences or goals. We introduce ExploreLLM that allows users to structure thoughts, help explore different options, navigate through the choices and recommendations, and to more easily steer models to generate more personalized responses. We conduct a user study and show that users find it helpful to use ExploreLLM for exploratory or planning tasks, because it provides a useful schema-like structure to the task, and guides users in planning. The study also suggests that users can more easily personalize responses with high-level preferences with ExploreLLM. Together, ExploreLLM points to a future where users interact with LLMs beyond the form of chatbots, and instead designed to support complex user tasks with a tighter integration between natural language and graphical user interfaces.

cs.HC↗

arXiv2023

Semantic Modelling of Organizational Knowledge as a Basis for Enterprise Data Governance 4.0 -- Application to a Unified Clinical Data Model

Individuals and organizations cope with an always-growing amount of data, which is heterogeneous in its contents and formats. An adequate data management process yielding data quality and control over its lifecycle is a prerequisite to getting value out of this data and minimizing inherent risks related to multiple usages. Common data governance frameworks rely on people, policies, and processes that fall short of the overwhelming complexity of data. Yet, harnessing this complexity is necessary to achieve high-quality standards. The latter will condition any downstream data usage outcome, including generative artificial intelligence trained on this data. In this paper, we report our concrete experience establishing a simple, cost-efficient framework that enables metadata-driven, agile and (semi-)automated data governance (i.e. Data Governance 4.0). We explain how we implement and use this framework to integrate 25 years of clinical study data at an enterprise scale in a fully productive environment. The framework encompasses both methodologies and technologies leveraging semantic web principles. We built a knowledge graph describing avatars of data assets in their business context, including governance principles. Multiple ontologies articulated by an enterprise upper ontology enable key governance actions such as FAIRification, lifecycle management, definition of roles and responsibilities, lineage across transformations and provenance from source systems. This metadata model is the keystone to data governance 4.0: a semi-automatised data management process that considers the business context in an agile manner to adapt governance constraints to each use case and dynamically tune it based on business changes.

cs.AI↗

arXiv2023

The Design Space of Generative Models

Card et al.'s classic paper "The Design Space of Input Devices" established the value of design spaces as a tool for HCI analysis and invention. We posit that developing design spaces for emerging pre-trained, generative AI models is necessary for supporting their integration into human-centered systems and practices. We explore what it means to develop an AI model design space by proposing two design spaces relating to generative AI models: the first considers how HCI can impact generative models (i.e., interfaces for models) and the second considers how generative models can impact HCI (i.e., models as an HCI prototyping material).

cs.AI↗

arXiv2023

The Prompt Artists

This paper examines the art practices, artwork, and motivations of prolific users of the latest generation of text-to-image models. Through interviews, observations, and a user survey, we present a sampling of the artistic styles and describe the developed community of practice around generative AI. We find that: 1) the text prompt and the resulting image can be considered collectively as an art piece prompts as art and 2) prompt templates (prompts with ``slots'' for others to fill in with their own words) are developed to create generative art styles. We discover that the value placed by this community on unique outputs leads to artists seeking specialized vocabulary to produce distinctive art pieces (e.g., by reading architectural blogs to find phrases to describe images). We also find that some artists use "glitches" in the model that can be turned into artistic styles of their own right. From these findings, we outline specific implications for design regarding future prompting and image editing options.

cs.HC↗

arXiv2023

A Word is Worth a Thousand Pictures: Prompts as AI Design Material

Recent advances in Machine-Learning have led to the development of models that generate images based on a text description.Such large prompt-based text to image models (TTIs), trained on a considerable amount of data, allow the creation of high-quality images by users with no graphics or design training. This paper examines the role such TTI models can playin collaborative, goal-oriented design. Through a within-subjects study with 14 non-professional designers, we find that such models can help participants explore a design space rapidly and allow for fluid collaboration. We also find that text inputs to such models ("prompts") act as reflective design material, facilitating exploration, iteration, and reflection in pair design. This work contributes to the future of collaborative design supported by generative AI by providing an account of how text-to-image models influence the design process and the social dynamics around design and suggesting implications for tool design

cs.HC↗

arXiv2023

Nooks: Social Spaces to Lower Hesitations in Interacting with New People at Work

Initiating conversations with new people at work is often intimidating because of uncertainty about their interests. People worry others may reject their attempts to initiate conversation or that others may not enjoy the conversation. We introduce a new system, Nooks, built on Slack, that reduces fear of social evaluation by enabling individuals to initiate any conversation as a nook -- a conversation room that identifies its topic, but not its creator. Automatically convening others interested in the nook, Nooks further reduces fears of social evaluation by guaranteeing individuals in advance that others they are about to interact with are interested in the conversation. In a multi-month deployment with participants in a summer research program, Nooks provided participants with non-threatening and inclusive interaction opportunities, and ambient awareness, leading to new interactions online and offline. Our results demonstrate how intentionally designed social spaces can reduce fears of social evaluation and catalyze new workplace connections.

cs.HC↗

arXiv2021

Empathosphere: Promoting Constructive Communication in Ad-hoc Virtual Teams through Perspective-taking Spaces

When members of ad-hoc virtual teams need to collectively ideate or deliberate, they often fail to engage with each others' perspectives in a constructive manner. At best, this leads to sub-optimal outcomes and, at worst, it can cause conflicts that lead to teams not wanting to continue working together. Prior work has attempted to facilitate constructive communication by highlighting problematic communication patterns and nudging teams to alter interaction norms. However, these approaches achieve limited success because they fail to acknowledge two social barriers: (1) it is hard to reset team norms mid-interaction, and (2) corrective nudges have limited utility unless team members believe it is safe to voice their opinion and that their opinion will be heard. This paper introduces Empathosphere, a chat-embedded intervention to mitigate these barriers and foster constructive communication in teams. To mitigate the first barrier, Empathosphere leverages the benefits of "experimental spaces" in dampening existing norms and creating a climate conducive to change. To mitigate the second barrier, Empathosphere harnesses the benefits of perspective-taking to cultivate a group climate that promotes a norm of members speaking up and engaging with each other. Empathosphere achieves this by orchestrating authentic socio-emotional exchanges designed to induce perspective-taking. A controlled study (N=110) compared Empathosphere to an alternate intervention strategy of prompting teams to reflect on their team experience. We found that Empathosphere led to higher work satisfaction, encouraged more open communication and feedback within teams, and boosted teams' desire to continue working together. This work demonstrates that ``experimental spaces,'' particularly those that integrate methods of encouraging perspective-taking, can be a powerful means of improving communication in virtual teams.

cs.HC↗

arXiv2021

Achieving High Throughput and Elasticity in a Larger-than-Memory Store

Millions of sensors, mobile applications and machines now generate billions of events. Specialized many-core key-value stores (KVSs) can ingest and index these events at high rates (over 100 Mops/s on one machine) if events are generated on the same machine; however, to be practical and cost-effective they must ingest events over the network and scale across cloud resources elastically. We present Shadowfax, a new distributed KVS based on FASTER, that transparently spans DRAM, SSDs, and cloud blob storage while serving 130 Mops/s/VM over commodity Azure VMs using conventional Linux TCP. Beyond high single-VM performance, Shadowfax uses a unique approach to distributed reconfiguration that avoids any server-side key ownership checks or cross-core coordination both during normal operation and migration. Hence, Shadowfax can shift load in 17 s to improve system throughput by 10 Mops/s with little disruption. Compared to the state-of-the-art, it has 8x better throughput (than Seastar+memcached) and avoids costly I/O to move cold data during migration. On 12 machines, Shadowfax retains its high throughput to perform 930 Mops/s, which, to the best of our knowledge, is the highest reported throughput for a distributed KVS used for large-scale data ingestion and indexing.

cs.DC↗

arXiv2020

Auditing Digital Platforms for Discrimination in Economic Opportunity Advertising

Digital platforms, including social networks, are major sources of economic information. Evidence suggests that digital platforms display different socioeconomic opportunities to demographic groups. Our work addresses this issue by presenting a methodology and software to audit digital platforms for bias and discrimination. To demonstrate, an audit of the Facebook platform and advertising network was conducted. Between October 2019 and May 2020, we collected 141,063 ads from the Facebook Ad Library API. Using machine learning classifiers, each ad was automatically labeled by the primary marketing category (housing, employment, credit, political, other). For each of the categories, we analyzed the distribution of the ad content by age group and gender. From the audit findings, we considered and present the limitations, needs, infrastructure and policies that would enable researchers to conduct more systematic audits in the future and advocate for why this work must be done. We also discuss how biased distributions impact what socioeconomic opportunities people have, especially when on digital platforms some demographic groups are disproportionately excluded from the population(s) that receive(s) content regulated by law.

cs.CY↗