Unverified50% confidenceConceptExact time
LLM-in-the-loop is a semi-automated paradigm for constructing evaluation data: large language models handle stages such as initial screening, rewriting, candidate option generation, and difficulty stratification, while human experts are responsible for final annotation, correction, and quality control.
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Long-term
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10/15/2026
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Related Claims
UnverifiedLLM-Loop (Large Language Model loop working mechanism) refers to embedding an AI large model in a continuously iterating feedback loop: the user states a requirement, the AI generates output, the user validates and provides feedback, and the AI corrects and outputs again, cycling until the goal is reached.78% similarUnverifiedLLMs are essentially pattern-matching systems based on statistical regularities, learning through pre-training on massive code corpora what code typically follows a given context.74% similarUnverifiedLoopWAM is a closed-loop world-action model capable of learning from deployment rollouts across the full quality spectrum, including low-quality data.73% similarUnverifiedA typical LLM uses an autoregressive generation approach, predicting token by token with each step's output feeding into the next step as input, forming a loop72% similarVerifiedUnlike rule-based static analysis tools (such as CodeQL) or symbolic execution tools, LLMs can understand comment intent, recognize the semantics of naming conventions, and infer data flow in the absence of complete type information.72% similar
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