Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 12·5 min read
Auditing Automated Evaluation, Error Propagation, and Runtime Mitigation in Tool-Using Language Agents
TL;DR
AgentProp-Bench, a diagnostic benchmark for evaluating tool-using large language model agents, reveals significant discrepancies in automated evaluations compared to human judgments, highlighting error propagation and fabrication issues in outputs.
✦ Why It Matters
Evaluate your LLM agents against human annotations to ensure reliability and accuracy in outputs.
Key Takeaways
- Use human annotations to validate automated evaluations of language models.
- Implement runtime interceptors to reduce output hallucinations.
- Be aware of potential fabrication in tool execution claims.
- Analyze parameter-level errors to improve final answer accuracy.
How It Works
AgentProp-Bench evaluates language model agents by comparing automated output assessments with human annotations. It employs a combination of models to enhance evaluation accuracy, revealing the limitations of simpler heuristic methods.