Local LLM Hits Perfect Score Yet Misses Every Answer

The post begins with the surprising claim that a locally run language model earned a perfect 6‑point score. Despite the flawless score, the model’s responses were consistently inaccurate. The author details the testing process used to evaluate the model’s output. Specific examples illustrate how the answers deviated from factual correctness. The discrepancy raises questions about the scoring methodology employed. The writer examines potential flaws in the evaluation criteria. The article discusses broader implications for trusting automated scoring systems. It ends by urging developers to scrutinize metrics before relying on them.