問題改寫的提示詞提升多跳問題的檢索效果，用戶輸入部分放到提示詞最后

發布于 2025-5-23 07:00

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在使用大模型處理多跳問題（multi-hop question）時，我們常常面臨一個挑戰：原始問題可能不夠具體或缺乏關鍵實體信息，導致語義搜索系統難以準確檢索到相關答案。為了解決這個問題，現在大家常使用問題改寫，獲取深層次的知識。下述是一套有效的問題改寫提示詞（prompt），專門用于“問題改寫”階段，幫助模型生成更清晰、更具實體導向的新問題。

這套提示詞經過實際測試，效果不錯。

實驗效果

qwen-2.5-7B 作為問題改寫的大模型。在 hotpot 數據集上的測試，1000條數據構建向量數據庫：

直接使用用戶問題在向量數據庫中做召回TopK@10 hite_rate命中率可以達到82%左右。
使用問題改寫后，TopK@10 + TopK@10 hite_rate命中率可以達到91%左右。

其實 qwen-2.5-7B的問題改寫能力不強，如果你不使用下述提示詞，會發現很多問題改寫都失敗了，無法獲得下一步的信息。但一些強大的大模型比如 gpt-4o等大參數的模型表現很好。

使用下述問題改寫的提示詞達到的效果，可以與gpt-4o問題改寫相媲美！

一、提示詞設計思路詳解

以下是我在項目中使用的提示詞模板，專門用于引導大模型進行高質量的問題改寫：

query_rewrite = """
You are given the following four elements:

1. **Original Question**
2. **Relevant Supporting Text(s)**

Your task is to **create a new, better question** that would help a semantic search system (like vector-based retrieval) find relevant information more accurately.

### ?? Follow These Clear Steps:

**Step 1: Understand the original question.**
Identify what the question is asking — focus on the key person, object, or event it refers to.

**Step 2: Extract the key detail from the supporting text.**
Look carefully at the relevant text and **find the most important new information** — especially **names**, dates, roles, or titles.
?? **You must include this key information in the new question.**

**Step 3: Create a natural follow-up question.**
Now, think of a new question that:

* Focuses on the subject identified from the relevant text (e.g., a person).
* Moves the conversation toward what the original question was looking for (but in a clearer or more direct way).

**Step 4: Write the new question clearly and completely.**
Your final question must:

* **Include the key entity or name (e.g., a person) from the relevant text.**
* Be directly connected to the original topic.
* Make it easier for a search system to retrieve the right answer.

### ?? Do Not:

* Leave out key names or details that were introduced in the relevant text.
* Repeat the original question exactly.

### ? Example (Just for Reference - Do Not Copy):

If the original question was:

> "Which team does the quarterback picked first in the 2010 draft play for?"

And the relevant text tells us:

> "Sam Bradford was taken first in the 2010 draft."

Then your new question **must include 'Sam Bradford'** and could be something like:

> "Which NFL team did Sam Bradford play for during the early 2010s?"

### ?? Output Format:
1. A multi-step, logically coherent explanation showing your reasoning process.
2. A json block at the end containing the final inferred question.

{{
  "new_question": "Your clearly written, specific, entity-rich question goes here."
}}

### Input Format:
- Question: {user_question}
- Relevant Texts: {relevant_texts}
""".lstrip()

這個提示詞的設計有幾個關鍵點：