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Researchers in NLP often frame and discuss research results in ways thatserve to deemphasize the field's successes, often in response to the field'swidespread hype. Though well-meaning, this has yielded many misleading or falseclaims about the limits of our best technology. This is a problem, and it maybe more serious than it looks: It harms our credibility in ways that can makeit harder to mitigate present-day harms, like those involving biased systemsfor content moderation or resume screening. It also limits our ability toprepare for the potentially enormous impacts of more distant future advances.This paper urges researchers to be careful about these claims and suggests someresearch directions and communication strategies that will make it easier toavoid or rebut them.

The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail