如何利用LLM或NLP模型识别用户指令中的单/多任务？

一、修复当前GPT-3.5提示词的问题 #

你的核心问题是提示词对「多任务」的定义覆盖不全，且示例场景单一，导致模型误判带触发条件的多任务指令。可以从以下两点优化提示词：

1. 明确多任务判定规则
   在提示词开头补充清晰的判定标准：

   • 单任务：仅包含一个需要执行的动作（无论是否带前置触发条件）
   • 多任务：包含两个及以上并列的执行动作，共享同一个触发条件或存在多个独立动作指令

2. 补充对应场景的示例
   加入你遇到的触发式多任务指令作为正确示例，让模型学习这类场景的判定逻辑。

优化后的提示词示例：

Carefully analyze the request to determine if it contains a single task or multiple tasks, then generate JSON output following these rules:
- **Single Task**: The request contains only one action to execute (regardless of whether it has a trigger condition).
- **Multiple Task**: The request contains two or more parallel actions to execute, either sharing the same trigger condition or being independent tasks.

### Example 1 (Query-type Multiple Task):
User Request: "how many executions happen with success and fail so far"
Analysis: This is a multiple task because it asks for two distinct counts: successful executions and failed executions.
JSON Output:
{
    "requests": [
        {
            "task": "how many executions happen with success and fail so far",
            "type": "multiple_task"
        }
    ]
}

### Example 2 (Trigger-based Multiple Task):
User Request: "When a New google calendar event is created, post a message to general channel in slack plus sync it to Salesforce leads"
Analysis: This is a multiple task because it requires executing two parallel actions (post to Slack, sync to Salesforce) under the same trigger condition (new Google Calendar event created).
JSON Output:
{
    "requests": [
        {
            "task": "When a New google calendar event is created, post a message to general channel in slack plus sync it to Salesforce leads",
            "type": "multiple_task"
        }
    ]
}

### Example 3 (Single Task):
User Request: "Post a message to the general Slack channel"
Analysis: This is a single task because it only contains one action.
JSON Output:
{
    "requests": [
        {
            "task": "Post a message to the general Slack channel",
            "type": "single_task"
        }
    ]
}

Now process the following user request:
User request: 

二、提示词工程vs微调的抉择建议 #

1. 优先选择提示词工程
   针对指令表述多样的问题，可通过场景化示例覆盖解决：收集业务中常见的指令类型（触发式、查询式、命令式等），为每种类型补充单/多任务的标注示例，让模型学习你的业务判定逻辑。这种方式成本低、迭代快，无需大规模数据集，适合初期快速验证。

2. 提示词达瓶颈时再考虑微调
   如果提示词优化后仍有大量边缘场景误判，再启动微调。此时无需构建大规模数据集，只需：

   • 收集业务中高频误判、边缘场景的指令
   • 为每条指令标注正确的「task」和「type」
   • 使用LoRA等轻量微调方法，基于GPT-3.5基础模型做针对性微调，既能降低计算成本，又能精准适配业务场景的判定需求

内容的提问来源于stack exchange，提问作者Rishabh Tripathi