Summary of Axolotl: Fairness Through Assisted Self-debiasing Of Large Language Model Outputs, by Sana Ebrahimi et al.
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs
by Sana Ebrahimi, Kaiwen Chen, Abolfazl Asudeh, Gautam Das, Nick Koudas
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Pre-trained Large Language Models (LLMs) have revolutionized natural language processing, but they are prone to biases in their training data, leading to unfair outcomes. To mitigate this issue, numerous strategies have been proposed, but they often require significant computational resources and may compromise model performance. This paper introduces AXOLOTL, a novel post-processing framework that operates agnostically across tasks and models, leveraging public APIs to interact with LLMs without direct access to internal parameters. Through a three-step process resembling zero-shot learning, AXOLOTL identifies biases, proposes resolutions, and guides the model to self-debias its outputs. This approach minimizes computational costs and preserves model performance, making AXOLOTL a promising tool for debiasing LLM outputs with broad applicability and ease of use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Large Language Models (LLMs) are super smart computers that can understand language. But sometimes, they learn biases from the data they’re trained on, which can lead to unfair outcomes. This is a problem! To fix it, researchers have proposed many solutions, but they often require a lot of computer power and might make the models less good at what they do. In this new study, scientists created a tool called AXOLOTL that helps LLMs avoid biases without needing lots of computer power or affecting how well they work. This is important because it makes it easier to use these super smart computers for many different tasks. |
Keywords
* Artificial intelligence * Natural language processing * Zero shot
