Unverified50% confidenceFactExact time
Large models do not actively update their weights during the inference phase and remain frozen after deployment.
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50%
Confidence
Long-term
Relevance
9/22/2026
First Seen
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Unverified大模型缺乏项目特定上下文时，其输出会退化为「有根据的猜测」，这是二次开发越改越乱的技术根源70% similarUnverifiedLarge models have high inference costs, large response latency, and steep deployment barriers, making them unsuitable for all scenarios.68% similarUnverifiedAs model capabilities improve and application scenarios become increasingly complex, relying solely on Prompt optimization can no longer meet production-level stability requirements.68% similarUnverified过于激进的上下文修剪可能导致模型遗忘重要前置条件，过于保守则无法实现有效token节省68% similarVerifiedDuring LoRA fine-tuning, the original model parameters are frozen and only the inserted low-rank matrices are updated, significantly reducing VRAM requirements and training costs.67% similar
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