The rapid adoption of language models (LMs) across diverse applications has raised concerns about their factuality, i.e., their consistency with real-world facts.We introduce VERIFY, an evidence-based evaluation pipeline that measures LMs' factuality in real-world user interactions.VERIFY considers the verifiability of LM-generated content and categorizes content units as Supported, Unsupported, or Undecidable based on Web-retrieved evidence.Importantly, factuality judgment by VERIFY more strongly correlates with human evaluations than existing methods.Using VER-IFY, we identify "hallucination prompts," i.e., those that frequently elicit factual errors in LM responses.These prompts form FACTBENCH, a dataset of 1K prompts spanning 150 topics and tiered into Easy, Moderate, and Hard prompts.We benchmark widely-used openweight and proprietary LMs from six families, yielding three key findings: (i) LMs' factual precision declines from Easy to Hard prompts, (ii) factuality does not necessarily improve with scale; Llama3.1-405B-Instructperforms comparably to or worse than its 70B variant, and (iii) Gemini1.5-Proshows a notably higher refusal rate, with over-refusal in 25% of cases.

Large language models (LLMs) excel at generating long-form responses, but evaluating their factuality remains challenging due to complex inter-sentence dependencies within the generated facts.Prior solutions predominantly follow a decompose-decontextualize-verify pipeline but often fail to capture essential context and miss key relational facts.In this paper, we introduce VERIFACT, a factuality evaluation framework designed to enhance fact extraction by identifying and resolving incomplete and missing facts to support more accurate verification results.Moreover, we introduce FACTR-BENCH 1 , a benchmark that evaluates both precision and recall in long-form model responses, whereas prior work primarily focuses on precision.FACTRBENCH provides reference fact sets from advanced LLMs and human-written answers, enabling recall assessment.Empirical evaluations show that VERIFACT significantly enhances fact completeness and preserves complex facts with critical relational information, resulting in more accurate factuality evaluation.Benchmarking various open-and close-weight LLMs on FACTRBENCH indicate that larger models within same model family improve precision and recall, but high precision does not always correlate with high recall, underscoring the importance of comprehensive factuality assessment.1.There is a demand for gold as jewelry 2. The price of gold could drop by 20-50% or more.3. Copper and silver are competitive with gold in terms of price.Anthropic.