Unverified50% confidenceFactExact time
Among three trap questions (blank license plate, rain-sound transcription, text injection), the model got only one right, exposing the hallucination risk that small models tend to "fill in" rather than refuse when input information is insufficient.
1
Sources
50%
Confidence
Long-term
Relevance
11/4/2026
First Seen
Sources
Running Audio and Vision Locally with a 2B Model: LFM3 Hands-On Revealed
bilibili枫叶边城11/4/2026
Related Entities
Related Claims
VerifiedWhen faced with ambiguous instructions, large models tend to fill information gaps with content that appears plausible but is unverified (hallucination completion), and explicitly writing out constraints and acceptance criteria can suppress this completion impulse.72% similarUnverifiedWhen third-party grading is satisfied but questions are too simple, a ceiling effect occurs, making it impossible to differentiate between models.71% similarUnverifiedStreaming models have more failure modes than batch models and often hallucinate additional speakers out of thin air, which is a direct consequence of insufficient context.71% similarUnverifiedQuantized models and sparse computation paths are sensitive to the numerical distribution of inputs; if only zero tensors are used during warmup, quantization operators may follow different code paths than those taken during real inference, causing memory estimation distortion.70% similarUnverifiedWhen the context window approaches its limit, the model does not directly throw an error; instead, it shows symptoms of quality degradation such as forgetting earlier instructions, duplicated work, broken logic, and increased hallucination.70% similar
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