24 lines
1.3 KiB
Markdown
24 lines
1.3 KiB
Markdown
# MultiAgent Debate
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Debate workflow simulates a multi-turn discussion between different agents, mostly several solvers and an aggregator.
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Typically, the solvers generate and exchange their answers, while the aggregator collects and summarizes the answers.
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We implement the examples in [EMNLP 2024](https://aclanthology.org/2024.emnlp-main.992/), where two debater agents
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will discuss a topic in a fixed order, and express their arguments based on the previous debate history.
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At each round a moderator agent will decide whether the correct answer can be obtained in the current iteration.
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## Setup
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The example is built upon DashScope LLM API in [main.py](https://github.com/agentscope-ai/agentscope/blob/main/examples/workflows/multiagent_debate/main.py).
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You can also change to the other LLMs by modifying the ``model`` and ``formatter`` parameters in the code.
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To run the example, first install the latest version of AgentScope, then run:
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```bash
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python examples/workflows/multiagent_debate/main.py
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```
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> Note: The example is built with DashScope chat model. If you want to change the model in this example, don't forget
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> to change the formatter at the same time! The corresponding relationship between built-in models and formatters are
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> list in [our tutorial](https://doc.agentscope.io/tutorial/task_prompt.html#id1) |