Search Results for author:
Found 9 papers, 4 papers with code
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks.
Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs
We find that holdouts, compared to early adopters matched on covariates, are 69% more likely to click on untrusted news sites.
When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
Finally, we showcase that using suggestion acceptance as a reward signal to know which suggestions to display leads to reduced quality suggestions indicating an unexpected pitfall.
Generation Probabilities Are Not Enough: Exploring the Effectiveness of Uncertainty Highlighting in AI-Powered Code Completions
Through a mixed-methods study with 30 programmers, we compare three conditions: providing the AI system's code completion alone, highlighting tokens with the lowest likelihood of being generated by the underlying generative model, and highlighting tokens with the highest predicted likelihood of being edited by a programmer.
Aligning Offline Metrics and Human Judgments of Value for Code Generation Models
Large language models have demonstrated great potential to assist programmers in generating code.
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction.
NL-EDIT: Correcting semantic parse errors through natural language interaction
We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors.
Leveraging Structured Metadata for Improving Question Answering on the Web
We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking.
Conversations with Documents. An Exploration of Document-Centered Assistance
Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.
