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Found 13 papers, 3 papers with code
Interactive Debugging and Steering of Multi-Agent AI Systems
Fully autonomous teams of LLM-powered AI agents are emerging that collaborate to perform complex tasks for users.
Challenges in Human-Agent Communication
Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment, invoke tools, and communicate with other agents to solve problems.
Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
Magentic-One uses a multi-agent architecture where a lead agent, the Orchestrator, plans, tracks progress, and re-plans to recover from errors.
AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous domains.
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions
However, creating high-quality datasets with LLMs can be challenging.
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing.
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.
How Different Groups Prioritize Ethical Values for Responsible AI
Compared to the US-representative sample, AI practitioners appear to consider responsible AI values as less important and emphasize a different set of values.
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible.
Machine Teaching: A New Paradigm for Building Machine Learning Systems
This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them.
Active Learning with Oracle Epiphany
We present a theoretical analysis of active learning with more realistic interactions with human oracles.
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Based on the machine's output, the teacher can revise the definition of the task or make it more precise.
