Summary of Prompt-based Bias Calibration For Better Zero/few-shot Learning Of Language Models, by Kang He et al.
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models
by Kang He, Yinghan Long, Kaushik Roy
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposed null-input prompting method aims to calibrate the intrinsic bias encoded in pre-trained language models, enhancing their performance in downstream zero/few-shot learning while emphasizing efficiency. By leveraging a diverse set of auto-selected null-meaning inputs generated from GPT-4, the method probes the intrinsic bias of pre-trained LMs and formulates a distribution disparity loss for bias calibration. Experimental results show that this approach promotes an equitable starting point for LMs, preserving language modeling abilities and significantly improving zero/few-shot learning performance in various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make language models fairer by reducing the bias they have learned from their training data. This can help them perform better when we give them only a few examples of what to do. The method works by asking the model questions that don’t make sense, which helps it learn to be more neutral. This approach is efficient and improves the model’s performance in many tasks. |
Keywords
* Artificial intelligence * Few shot * Gpt * Prompting
