Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Jul 27·5 min read
InteractComp: Evaluating Search Agents With Ambiguous Queries
TL;DR
InteractComp is a new benchmark for evaluating search agents' ability to handle ambiguous queries through interactive clarification. It reveals that current models struggle significantly with ambiguity, achieving only 13.73% accuracy without interaction.
✦ Why It Matters
Evaluate your search agents using InteractComp to enhance their interaction capabilities with ambiguous queries.
Key Takeaways
- Incorporate InteractComp to assess search agent performance on ambiguous queries.
- Focus on developing interactive mechanisms for search agents.
- Monitor and improve interaction capabilities over time.
- Address overconfidence in models to enhance reasoning skills.
How It Works
InteractComp uses a target-distractor methodology to create 210 expert-curated questions that introduce ambiguity. Agents must interact with users to clarify these ambiguities, allowing for a more realistic evaluation of their capabilities.