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DeepSeek Distilled 32B Responding in Multi Language on English Prompting
Here is the response for one of prompts (I got more responses in multi language)
{think}
Okay, so I'm trying to figure out how to use the text to fill in the blank schema so that it ends up looking exactly like the filled schema. Let me start by understanding what each part is.
First, there's the Text and the Blank Schema. The Blank Schema must be a JSON structure with placeholders or empty fields where data needs to be filled in. Then, the Filled Schema is an example of what the final output should look like after filling in those blanks.
Looking at the goals, I need to compare both schemas and explain step-by-step how to use the text (which might contain instructions or content) to fill in the blank schema correctly. At the end, I have to ensure that my reasoning leads to a filled schema identical to the given one and explain why.
So, I'll start by examining the Blank Schema's structure. It probably has sections like根节点,children, id, title等。然后,我会看看在Filled Schema中,这些部分是如何填充的。例如,在Blank Schema中可能有空的components数组,而在Filled Schema中,components被填写了具体的类型、元数据和属性。
接下来,我需要确定Text中是否有特定的指导或内容可以帮助填充这些字段。比如,如果Text里有一段关于Compliance Risks的内容,那么在对应的节点下,components的content字段就会填上这段文字。
然后,我会检查变量部分。在Filled Schema中,每个component下的variables可能有具体的键值对。例如,在CHECKBOX组件里,有一个name为Confirmation、value为true的变量。这可能意味着Text中有特定的指示指出该复选框应被选中,或者在内容中提到这是一个确认点。
我还注意到,每个节点都有唯一的id,像是1.0, 2.1.1这样的结构,按照层次递增。因此,填充时需要确保id严格按照这个规律生成,避免重复或错误。
再看看content和metadata的区别。Content通常是放在component里的文本内容,而metadata可能包含其他类型的描述或标签信息,比如
i think the downside of thinking models is that even for simple question they may take alot of thinking tokens but i think we should have dataset to Train llms to figure out when to use thinking strategy and when to simply answer the question like regular llms do