Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 28·4 min read
Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Run new adversarial benchmark before production LLM deployments.
- Expect 15-25% accuracy drop versus MMLU on reasoning tasks.
- Open-source models degrade faster — prefer fine-tuned variants.
- Dataset available under CC-BY for custom evaluation pipelines.
Full Summary
Prior LLM benchmarks relied on static multiple-choice datasets that saturate quickly. The team constructed adversarial prompts by combining chain-of-thought traps with factual distractors.
GPT-4 scored 71% on the new benchmark versus 89% on MMLU. Open-source models showed steeper drops, suggesting benchmark overfitting.
The dataset is released under CC-BY for reproducibility.