It is a preliminary implementation of the paper "Improving Factuality and Reasoning in Language Models through Multiagent Debate," which aims to enhance the accuracy and reasoning capabilities of language models by employing a multiagent debate framework.
It is a preliminary implementation of the paper “Improving Factuality and Reasoning in Language Models through Multiagent Debate,” which aims to enhance the accuracy and reasoning capabilities of language models by employing a multiagent debate framework. This approach involves multiple agents engaging in structured debates to refine and validate responses, thereby improving the factual correctness and logical coherence of the model’s outputs. The project is part of ICML 2024 and is developed by Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch.
The implementation includes code for running experiments on various tasks such as arithmetic, Grade School Math (GSM), biographies, and the Massive Multitask Language Understanding (MMLU) dataset. Each task has dedicated subfolders containing scripts to generate and evaluate answers using the multiagent debate method. For example, to generate answers for math problems, users can navigate to the math directory and run `python gen_math.py`. Similarly, for GSM tasks, the `gen_gsm.py` script generates answers, while `eval_gsm.py` evaluates the results. The GSM and MMLU datasets are available for download, and users can also explore additional debate logs and an open-source implementation by gauss5930.
The project encourages feedback and provides a BibTeX file for citing the paper. It is hosted on GitHub under the repository `composable-models/llm_multiagent_debate`, where users can access the latest updates, documentation, and resources. The repository includes navigation menus for searching code, repositories, users, issues, and pull requests, as well as options to provide feedback and use saved searches for quicker filtering of results. The project is actively maintained by five contributors and is open for further exploration and experimentation.
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