Summary of Chatgpt Based Data Augmentation For Improved Parameter-efficient Debiasing Of Llms, by Pengrui Han et al.
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs
by Pengrui Han, Rafal Kocielnik, Adhithya Saravanan, Roy Jiang, Or Sharir, Anima Anandkumar
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to debiasing Large Language Models (LLMs) using ChatGPT to generate synthetic training data. The authors propose two strategies: Targeted Prompting and General Prompting, which aim to enhance the debiasing of LLMs while preserving their multi-task language capabilities. They leverage resource-efficient LLM debiasing using adapter tuning and compare the effectiveness of their approach with existing debiasing datasets. The results show that synthetic data produced via this approach surpasses existing datasets in debiasing performance, while also preserving internal knowledge of a pre-trained LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make language models fairer by making them less biased towards certain groups or categories. It does this by using a chatbot called ChatGPT to create fake training data that can help remove biases from the model. The authors tested two ways to do this: one way is more focused, and another way is more general. They found that the synthetic data they created worked better than existing methods at removing biases while still keeping the model’s language abilities. |
Keywords
» Artificial intelligence » Multi task » Prompting » Synthetic data
