Unverified50% confidenceFactExact time
The model outputs the most "likely" continuation token by token by probability rather than verifying whether a package actually exists in the npm registry, so package names, function signatures, and API version numbers are high-hallucination areas.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedToken count can serve as a proxy metric, because genuine code review requires steps such as reading context, comparing changes, and building a reasoning chain that produce observable computational consumption, while judging without reviewing presents a statistically anomalously low value.67% similarUnverifiedModel names that unexpectedly appear in the API may be genuine internal testing codenames, but could also be cache remnants, placeholders, or pranks.65% similarUnverifiedUsing another vendor's tokenizer library to compute the target model's token count leads to systematic bias, because different models use different vocabularies, and this bias is unobservable from the caller's perspective.63% similarUnverifiedConstrained decoding or classification head methods allow the model to output only the highest-probability label from a limited set of candidates, skipping the process of generating free text token by token63% similarUnverifiedAttributing a technology stack solely based on model identifiers intercepted from front-end network requests has limitations, because identifiers may be wrapped by intermediate layers.62% similar
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