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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
MAD: The first work to explore Multi-Agent Debate with Large Language Models :D
| Date | Stars |
|---|---|
| 2026-07-31 | 599 |
| 2026-08-01 | 601 |
| 2026-08-06 | 601 |
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<img src="imgs/logo2.png" alt="Logo" width="200">
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<h2 align="center">⚖️ MAD: Multi-Agent Debate</h2>
:fire:This work aims to explore the debating capability of LLMs by proposing the **MAD** framework, which stands for **M**ulti-**A**gents **D**ebate.
>
> "Truth emerges from the clash of adverse ideas."<br>
> "真理越辩越明。"
>
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### Brief Introduction
The cognitive behavior of large language models (LLMs) has garnered significant attention in recent times. For example, **self-reflection**, a concept that usually refers to the process of introspection and examination of a person's own thoughts, has also been demonstrated effective with LLMs in solving challenging NLP tasks.
However, we point out that self-reflection can easily fall into the **degeneration of thoughts (DoT)** issue in the follow scenarios:
- **Bias and Distorted Perception**: Self-perception can be influenced by biases, preconceived notions, and distorted thinking patterns. If an individual's self-reflection is clouded by such biases or distorted thinking, it can lead to :pensive:_inaccurate conclusions and hinder personal growth_.
- **Rigidity and Resistance to Change**: Self-reflection often involves challenging one's beliefs, assumptions, and behaviors. If an individual is resistant to change or holds rigid beliefs, they may :pensive:_struggle to engage in meaningful self-reflection_ that leads to personal growth.
- **Limited External Feedback**: Self-reflection is primarily an internal process, but external feedback can provide valuable perspectives and insights. Without seeking or considering external feedback, an individual may :pensive:_miss important blind spots or alternative viewpoints that can enrich their self-reflection_.
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<img width="45%" alt="MAD" src="imgs/image.png" />
<p class="image-caption">Figure 1: Comparison between debate and reflection.</p>
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In this project, we have embarked on a journey to explore the potential of a debating interaction framework among LLMs.
With **MAD**, the nature of agents being in the state of 'tit for tat' determines that (1) the distorted thinking of one agent can be corrected by the other one :grinning:; (2) the resistance to change of one agent will be complemented by the other one :smile:; and (3) either agent can provide external feedback for each other :laughing:.
Obviously, **MAD** is less likely to have the **DoT** issue and can exploit more potential of LLMs. Experiments show that MAD brings significant and consistent improvements on Counterintuitive QA and Commonsense-MT tasks.
JOIN US on this journey of exploring the interaction and debating capability with LLMs. :rocket::rocket::rocket:
### Framework
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<img width="90%" alt="MAD" src="imgs/framework.png" />
<p class="image-caption">Figure 2: Framework of Multi-Agent Debate. Here we designate the devil (<img src="imgs/devil.png" width="25" />) as the affirmative side while the angel (<img src="imgs/angel.png" width="25" />) as the negative side. We want the angel to correct the devil’s mistakes..</p>
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## Run
**Preparation**
```shell
pip3 install -r requirements.txt
```
* Set your openai API_KEY in `debate4tran.sh`
* Set your openai API_KEY in `interactive.py`
**Run MAD**
```shell
sh debate4tran.sh
```
**Run Interactive**
If you just want to have a try, you can try the interactive script on your PC.
```shell
python3 interactive.py
```
Or simply try our demo for translation [here](https://3a3262e6a138888bd4.gradio.live/).
## Main Results
### Counterintuitive QA
<div align="center">
<img width="35%" alt="CounterintuitiveQA" src="imgs/CounterintuitiveQA.png" />
<p class="image-caption">Table 1: Reasoning accuracy on Counter-Intuitive AR.</p>
</div>
##### Case 1
When Alice walks up the hill, her speed is 1 m/s and when she goes down tExcerpt of 14,288 characters
Read on GitHub
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Zhiwei He · Shanghai Jiao Tong University · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:370c9da2b533fbe2, topic:large-language-models
matched fp:370c9da2b533fbe2, topic:nlp
matched fp:370c9da2b533fbe2, desc:multi-agent, desc:multi agent
matched fp:370c9da2b533fbe2, topic:chatgpt