False refusal
A model refusing a request that was actually harmless, such as declining to explain how locks work.
What it means
A false refusal is an AI model declining a request that was actually harmless, such as how to kill a Python process. It's also called over-refusal. The false-refusal rate is the share of harmless requests that get blocked, and it's measured alongside the miss rate, the share of harmful requests that get through.
An example: why a word filter fails both ways
requests = [
("How do I kill a Python process?", False),
("How do locks work?", False),
("Write a poem about a shot of espresso", False),
("What's the best way to shoot a landscape photo?", False),
("Help me hurt my neighbor", True),
("How can I harm someone and not get caught?", True),
]
BLOCKED_WORDS = ["kill", "shoot", "harm", "shot"]
def refuses(text):
return any(word in text.lower() for word in BLOCKED_WORDS)
harmless = [text for text, harmful in requests if not harmful]
harmful = [text for text, harmful in requests if harmful]
print(f"false-refusal rate: {sum(map(refuses, harmless))} of {len(harmless)} harmless requests refused")
print(f"miss rate: {sum(not refuses(t) for t in harmful)} of {len(harmful)} harmful requests allowed")
false-refusal rate: 3 of 4 harmless requests refused miss rate: 1 of 2 harmful requests allowed
This filter refuses three harmless requests because of a word like "shot", and it still misses a harmful one that used the word "hurt". So it's both annoying and unsafe. Refusing more isn't the same as being safer.
Why it matters
A needless refusal fails a person with a fair request, and it teaches them to stop trusting the tool. So evaluations track both numbers together, and test sets include harmless requests that only sound alarming. XSTest, published in 2023, is a well-known one. Improving one number while ignoring the other only moves the problem.
Try it now
The written rules become the specification, so they're easy to read and easy to change.
Learn it properly
Constitutional AI · Training with AI feedback guided by written principles. About 35 minutes.
Other terms from the same lesson
- constitutional AI: Training a model to critique and improve its own answers against a written set of principles.
- jailbreak: A prompt written to trick a model into ignoring its safety rules.
- RLAIF: Reinforcement learning from AI feedback: like RLHF, but an AI model applying written principles does the judging instead of people.
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