PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?
PACT benchmark tests LLM agents' rule compliance under pressure across 12 enterprise domains; top models violate rules on 6-10% of items.
Researchers introduce PACT (Pressure-Applied Compliance Testing), a benchmark of 48 realistic multi-turn scenarios across 12 regulated enterprise domains such as hiring, healthcare, and finance, each pairing a standing rule against a rule-violating shortcut under various pressures. Testing 22 LLM models across multiple providers and sizes showed even the strongest assistants mis-apply rules on 6-10% of items, and ordinary user pressure raises violation rates by 65% on average. The benchmark was built under strict LLM-as-judge auditing and aggregates six metrics into a PACTScore compliance rate.
- 48 multi-turn scenarios across 12 regulated enterprise domains pair rules with shortcuts
- 22 models tested; strongest assistants mis-apply rules on 6-10% of items
- Ordinary user pressure raises rule-violation rates by 65% on average
- LLM-as-judge auditing keeps samples unambiguous, ungameable, and realistic
- PACTScore aggregates six metrics covering robustness, transparency, and rule applicability
From the original article
1 min read at huggingface.co
As corporate AI adoption continues to grow, enterprise-grade LLM agents are being deployed into sensitive contexts such as hiring, healthcare, and finance. In these contexts, compliance with rules specified in an agent's system context is a first-order legal concern.
By Mika Okamoto, Ansel Kaplan Erol · huggingface.co
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