Factuality

真實性

LLMs have a tendency to generate responses that sounds coherent and convincing but can sometimes be made up. Improving prompts can help improve the model to generate more accurate/factual responses and reduce the likelihood to generate inconsistent and made up responses.

LLMs有時會產生聽起來連貫且有說服力的回覆，但有時可能是虛構的。改進提示可以幫助改善模型以產生更準確/真實的回覆，並減少產生不一致和虛構回覆的可能性。

Some solutions might include:

一些解決方案可能包括：

• provide ground truth (e.g., related article paragraph or Wikipedia entry) as part of context to reduce the likelihood of the model producing made up text.
• configure the model to produce less diverse responses by decreasing the probability parameters and instructing it to admit (e.g., "I don't know") when it doesn't know the answer.
• provide in the prompt a combination of examples of questions and responses that it might know about and not know about

• 提供真實的背景資訊（例如相關文章段落或維基百科條目），以減少模型產生虛假文字的可能性。
• 透過降低機率引數並指示模型在不知道答案時承認（例如，“我不知道”）來配置模型以產生較少多樣化的響應。
• 在提示中提供問題和答案的組合示範，包括可能已知和未知的內容。

Let's look at a simple example:

讓我們來看一個簡單的例子：

Prompt:

提示:

Q: What is an atom? 
A: An atom is a tiny particle that makes up everything. 

Q: Who is Alvan Muntz? 
A: ? 

Q: What is Kozar-09? 
A: ? Q: 

How many moons does Mars have? 
A: Two, Phobos and Deimos. 

Q: Who is Neto Beto Roberto? 

Output:

輸出:

A: ?

I made up the name "Neto Beto Roberto" so the model is correct in this instance. Try to change the question a bit and see if you can get it to work. There are different ways you can improve this further based on all that you have learned so far.

我編造了“Neto Beto Roberto”這個名字，所以在這種情況下模型是正確的。試著稍微改變一下問題，看看能否讓它起作用。根據你到目前為止所學到的一切，有不同的方法可以進一步改進它。