Explain FActScore's approach to long-form factual evaluation and its design decisions

**FActScore** is a way to measure how factually accurate a long piece of model-generated text is, like a biography or a multi-paragraph answer. The problem it solves: you cannot just string-match a long passage against a gold answer, and asking a judge 'is this accurate?' gives one fuzzy number that hides where the errors are. It works in three steps. First, decompose: break the text into atomic facts, the smallest claims you can check on their own. The sentence 'Marie Curie was born in Warsaw in 1867' splits into two facts: she was born in Warsaw, and she was born in 1867. Second, verify: for each atomic fact, look up relevant context in a trusted knowledge source like Wikipedia, then decide if the source supports it, contradicts it, or says nothing. Third, score: FActScore is simply the fraction of atomic facts that the source supports. A passage with 20 facts where 18 check out scores 0.9. The big payoff is localization. You learn which claims were hallucinated, not just that the passage was 'somewhat wrong.' The catch: it only works when the knowledge source covers the topic, and a lookup per fact is slow and costly.

**FActScore** evaluates the factual accuracy of long-form generations through three stages. First, decompose. The text is broken into atomic facts, the smallest independently verifiable claims. 'Marie Curie was born in Warsaw in 1867' becomes two facts. Decomposition is usually done by an LLM prompted to extract claims. Second, verify. For each atomic fact, retrieve relevant passages from an external knowledge source such as Wikipedia. An NLI model or LLM then labels the fact as supported, contradicted, or not-enough-info. Grounding on an external source, not the model's own text, avoids self-referential grading. Third, score. FActScore is the fraction of atomic facts that are supported: $$\text{FActScore} = \frac{\#\,\text{supported atomic facts}}{\#\,\text{total atomic facts}}$$ Granularity is the key win. Per-claim scoring localizes exactly which claims hallucinated. Holistic paragraph scoring cannot do that. The limitations are real. Verification quality collapses for topics the source covers poorly, like recent events or niche people. The pipeline is also expensive, since every fact needs retrieval plus a classification call. Decomposition itself can be noisy, splitting unevenly or dropping context.