497 citations · 1.9k across the 35 of their papers we have counts for
35 papers
Can LLM Code Explanations Adapt to Diverse Problem-Solvers' Needs?
Andrew Anderson, David Piorkowski, Justin Weisz +2
Large language model (LLM) code explanations can support people in solving code-related problems, yet prior work has shown that people have diverse problem-solving styles. If expla…
AgentSCOPE: Evaluating Contextual Privacy Across Agentic Workflows
Ivoline C. Ngong, Keerthiram Murugesan, Swanand Kadhe +3
Agentic systems are increasingly acting on users' behalf, accessing calendars, email, and personal files to complete everyday tasks. Privacy evaluation for these systems has focuse…
Story Arena: A Multi-Agent Environment for Envisioning the Future of Software Engineering
Justin D. Weisz, Michael Muller, Kush R. Varshney
What better way to understand the impact of AI on software engineering than to ask AI itself? We constructed Story Arena, a multi-agent "writer's room" in which multiple AI agents,…
Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
Hyo Jin Do, Rachel Ostrand, Werner Geyer +3
Large language models (LLMs) are susceptible to generating inaccurate or false information, often referred to as "hallucinations" or "confabulations." While several technical advan…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
An LLM's Attempts to Adapt to Diverse Software Engineers' Problem-Solving Styles: More Inclusive & Equitable?
Andrew Anderson, David Piorkowski, Margaret Burnett +1
Software engineers use code-fluent large language models (LLMs) to help explain unfamiliar code, yet LLM explanations are not adapted to engineers' diverse problem-solving needs. W…
Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation
Jessica He, Stephanie Houde, Justin D. Weisz
AI systems powered by large language models can act as capable assistants for writing and editing. In these tasks, the AI system acts as a co-creative partner, making novel contrib…
Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents
Ivoline Ngong, Swanand Kadhe, Hao Wang +4
Conversational agents are increasingly woven into individuals' personal lives, yet users often underestimate the privacy risks associated with them. The moment users share informat…
Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach
Stephanie Houde, Kristina Brimijoin, Michael Muller +6
Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, t…
Scopes of Alignment
Kush R. Varshney, Zahra Ashktorab, Djallel Bouneffouf +2
Much of the research focus on AI alignment seeks to align large language models and other foundation models to the context-less and generic values of helpfulness, harmlessness, and…
Position: Theory of Mind Benchmarks are Broken for Large Language Models
Matthew Riemer, Zahra Ashktorab, Djallel Bouneffouf +4
Our paper argues that the majority of theory of mind benchmarks are broken because of their inability to directly test how large language models (LLMs) adapt to new partners. This…
Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise
Justin D. Weisz, Shraddha Kumar, Michael Muller +4
AI assistants are being created to help software engineers conduct a variety of coding-related tasks, such as writing, documenting, and testing code. We describe the use of the wat…
Expedient Assistance and Consequential Misunderstanding: Envisioning an Operationalized Mutual Theory of Mind
Justin D. Weisz, Michael Muller, Arielle Goldberg +1
Design fictions allow us to prototype the future. They enable us to interrogate emerging or non-existent technologies and examine their implications. We present three design fictio…
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions
Hyo Jin Do, Rachel Ostrand, Justin D. Weisz +5
While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advanc…
Design Principles for Generative AI Applications
Justin D. Weisz, Jessica He, Michael Muller +3
Generative AI applications present unique design challenges. As generative AI technologies are increasingly being incorporated into mainstream applications, there is an urgent need…
The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development
Steven I. Ross, Fernando Martinez, Stephanie Houde +2
Large language models (LLMs) have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natura…
A Case Study in Engineering a Conversational Programming Assistant's Persona
Steven I. Ross, Michael Muller, Fernando Martinez +2
The Programmer's Assistant is an experimental prototype software development environment that integrates a chatbot with a code editor. Conversational capability was achieved by usi…
Toward General Design Principles for Generative AI Applications
Justin D. Weisz, Michael Muller, Jessica He +1
Generative AI technologies are growing in power, utility, and use. As generative technologies are being incorporated into mainstream applications, there is a need for guidance on h…
Better Together? An Evaluation of AI-Supported Code Translation
Justin D. Weisz, Michael Muller, Steven I. Ross +5
Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from co…
Investigating Explainability of Generative AI for Code through Scenario-based Design
Jiao Sun, Q. Vera Liao, Michael Muller +4
What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative mo…
Using Document Similarity Methods to create Parallel Datasets for Code Translation
Mayank Agarwal, Kartik Talamadupula, Fernando Martinez +5
Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has…
Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
Shweta Narkar, Yunfeng Zhang, Q. Vera Liao +2
Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models.…
Perfection Not Required? Human-AI Partnerships in Code Translation
Justin D. Weisz, Michael Muller, Stephanie Houde +5
Generative models have become adept at producing artifacts such as images, videos, and prose at human-like levels of proficiency. New generative techniques, such as unsupervised ne…
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks
April Yi Wang, Dakuo Wang, Jaimie Drozdal +6
Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code,…