Unverified50% confidenceFactExact time
There is a gap between the output confidence of language models and their actual accuracy, a phenomenon known in machine learning as "miscalibration."
1
Sources
50%
Confidence
Long-term
Relevance
10/17/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe overconfidence of a model answering directly while skipping retrieval is technically referred to as miscalibration, meaning there is a systematic deviation between confidence and actual accuracy.79% similarUnverifiedIn machine learning evaluation, precision and recall often have a trade-off relationship, and increasing recall typically leads to more genuine products being misclassified.78% similarUnverifiedExisting LLM-based automated RCA methods suffer from low diagnostic accuracy, which further deteriorates as execution traces grow longer.76% similarUnverifiedResearch has found that large language models tend to be overconfident when not specifically optimized, meaning they output high confidence scores but have a relatively high actual error rate.75% similarUnverifiedWith only a relatively small amount of targeted training data, it is possible to make a large language model give entirely incorrect answers to clearly stated facts75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1055033API
curl https://kongchang.com/api/v1/knowledge/claims/1055033MCP
get_claim(id=1055033)