13 citations · 26 across the 8 of their papers we have counts for
8 papers
The Invisible Mentor: Inferring User Actions from Screen Recordings to Recommend Better Workflows
Litao Yan, Andrew Head, Ken Milne +4
Many users struggle to notice when a more efficient workflow exists in feature-rich tools like Excel. Existing AI assistants offer help only after users describe their goals or pro…
TableTalk: Scaffolding Spreadsheet Development with a Language Agent
Jenny T. Liang, Aayush Kumar, Yasharth Bajpai +7
Spreadsheet programming is challenging. Programmers use spreadsheet programming knowledge (e.g., formulas) and problem-solving skills to combine actions into complex tasks. Advance…
Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products
Nadia Nahar, Christian Kästner, Jenna Butler +3
Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous chall…
Evaluating the Evaluator: Measuring LLMs' Adherence to Task Evaluation Instructions
Bhuvanashree Murugadoss, Christian Poelitz, Ian Drosos +5
LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to widespread use…
Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
Bhavya Chopra, Yasharth Bajpai, Param Biyani +4
The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption. Conversational interactions with LLM…
Building Your Own Product Copilot: Challenges, Opportunities, and Needs
Chris Parnin, Gustavo Soares, Rahul Pandita +3
A race is underway to embed advanced AI capabilities into products. These product copilots enable users to ask questions in natural language and receive relevant responses that are…
Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities
Bhavya Chopra, Ananya Singha, Anna Fariha +4
Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacl…
Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs
Ananya Singha, José Cambronero, Sumit Gulwani +2
Large language models (LLMs) are increasingly applied for tabular tasks using in-context learning. The prompt representation for a table may play a role in the LLMs ability to proc…