Una golden response es una respuesta de referencia conocida o considerada de alta calidad para un prompt o tarea determinada. Se usa en evaluaciones para comparar salidas de modelos con el comportamiento, corrección o estilo esperados.
A response known to be good. For example, given the following prompt: 2 + 2 The golden response is hopefully: 4 [!NOTE] Note: Some organizations define additional terms such as silver response and platinum response for responses of lower or higher quality, respectively, than the golden response. For example, an organization might use *platinum response* to indicate a golden response generated by an expert and then further vetted by other experts. #### Click here for notes about golden response and reference text. Some evaluation metrics, such as ROUGE, compare reference text to a model's generated text. When there is a single right answer to a prompt, the golden response typically serves as the reference text. Some prompts have no one right answer. For example, the prompt Summarize this document would likely have many right answers. For such prompts, reference text is often impractical because a model can generate a very wide range of possible summaries. However, a golden response might be helpful in this situation. For example, a golden response containing a good document summary can help train an autorater to discover patterns of good document summaries.