newsReddit r/artificialTrust 52 · CommunityPublished 10d agoLive · 10d ago
Why Self-Correction Loops Can Degrade Reliability in LLM Pipelines (85% Down to 62%)
In structured data extraction, adding an LLM-as-a-judge self-correction loop is often expected to improve accuracy. In practice, our pipeline showed the opposite: standalone extraction scored ~85% consistency , but introducing a validation/retry loop dropped consistency to 62% or lower. Architecture & Testing: Model Setup: GPT-5.4 used across separate instances for the extractor
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- PossiblePossibly related (embedding) · 53%Evaluate a model properly →
- PossiblePossibly related (embedding) · 47%ndcorder/outputguard →
- PossiblePossibly related (embedding) · 47%Form, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code Models →
- PossiblePossibly related (embedding) · 45%Judge, Retrieve, or Abstain: Uncertainty-Guarded LLM Judging with Provable Risk Guarantees →
- PossiblePossibly related (embedding) · 46%Asymmetric Capacity Allocation in Self-Refinement Pipelines →
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tutorialEvaluate a model properlyrepondcorder/outputguardpaperForm, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code ModelspaperJudge, Retrieve, or Abstain: Uncertainty-Guarded LLM Judging with Provable Risk Guarantees
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paperForm, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code ModelspaperJudge, Retrieve, or Abstain: Uncertainty-Guarded LLM Judging with Provable Risk Guaranteesrepondcorder/outputguardtutorialEvaluate a model properlypaperAsymmetric Capacity Allocation in Self-Refinement Pipelines