Explain why string-match accuracy is systematically misleading for LLM evaluation

**Exact string-match** asks one question: does the model's output equal the reference answer character for character? For multiple-choice or a single number, that works fine. For open-ended generation, it breaks badly. The structural reason is that any correct answer has many valid surface forms. You can paraphrase it, swap synonyms, reorder clauses, add a polite preamble, all while staying correct. Exact-match scores every one of those as wrong because the letters differ from the single reference string. There is a second problem. Quality is not one thing. A good LLM answer is faithful (claims grounded in fact or context), relevant (it addresses the question), complete (it covers what was asked), and appropriate in tone. A single reference string cannot encode all those axes at once. So a good evaluator does two things. It matches on meaning, not exact characters, using embeddings or a natural-language-inference model to check semantic equivalence. And it scores several quality dimensions separately rather than collapsing everything into one pass or fail. That usually means an LLM-as-judge with a structured rubric, backed by human spot-checks.

**Exact string-match** fails on open-ended outputs for a structural reason. There is no single gold answer. Any correct response has many semantically valid surface forms: paraphrases, synonym swaps, reordered clauses, different verbosity. Exact-match marks all but one as wrong. So the metric penalizes wording, not correctness. It produces a systematically pessimistic and noisy score that tracks lexical overlap with one reference, not actual answer quality. The deeper issue is that quality is multi-dimensional. A good answer must be faithful, relevant, complete, and tonally appropriate, all at once. One reference string cannot encode those axes. A response can match the reference on words yet be incomplete; another can diverge in words yet be fully correct. A good evaluator accounts for both. It checks semantic equivalence, not surface form, via embedding similarity or an entailment model. It scores each quality dimension independently rather than collapsing them into a binary. When those signals need general intelligence to assess, the field uses LLM-as-judge with structured rubrics. Cheap automated metrics handle what is auto-checkable. Human spot-checks cover what needs domain expertise.