Unverified50% confidenceOpinionExact time
When information is insufficient, the content generated by large models tends to go off track; the poorer the context, the worse the output quality.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
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Related Claims
UnverifiedThe larger the context window, the more likely model performance is to degrade—the more information fed to the model, the worse it may perform.79% similarUnverifiedThere is a fundamental trade-off between low-cost data collection and data quality; cheap but excessively noisy data may actually harm final model performance.79% similarUnverifiedAt the time of a model's initial release, it is allocated large capacity with few users, resulting in abundant idle compute that delivers extremely fast speeds, which cannot reflect long-term real-world performance.77% similarUnverifiedTraditional frame-sampling methods suffer from significant information loss, lack of temporal reasoning, and high computational costs.75% similarUnverifiedIn information retrieval, increasing recall tends to decrease precision, and vice versa.75% similar
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