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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
TL;DR
HarnessOpt-Bench is a new benchmark designed to evaluate the optimization of harnesses surrounding large language models (LLMs). It reveals significant variability in LLM performance across different tasks and setups, highlighting the potential for improvement in harness optimization.
✦ Why It Matters
Start evaluating your LLMs with HarnessOpt-Bench to identify optimization opportunities in your AI systems today.
Key Takeaways
- Implement HarnessOpt-Bench to assess LLM harness optimization.
- Explore different harness configurations to improve model performance.
- Utilize graded evaluation feedback for iterative improvements.
- Analyze performance variability across tasks to guide future optimizations.
How It Works
HarnessOpt-Bench operates by providing an optimizer, which is an LLM paired with a coding harness, a seed harness, and a fixed evaluation budget. The optimizer iteratively edits the harness based on feedback and submits a final candidate for scoring against a hidden test set, ensuring a fair evaluation process.