Summary of Take Care Of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction, by Ziyang Xu et al.
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction
by Ziyang Xu, Keqin Peng, Liang Ding, Dacheng Tao, Xiliang Lu
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 This paper investigates the “prompt bias” issue in pre-trained language models (PLMs), which can lead to inaccurate factual knowledge extraction. The authors show that all prompts exhibit non-negligible bias, with gradient-based prompts like AutoPrompt and OptiPrompt displaying significantly higher levels of bias. They also find that prompt bias can amplify benchmark accuracy unreasonably by overfitting test datasets, especially on imbalanced datasets like LAMA. To mitigate this issue, the authors propose a representation-based approach to remove biased representations during inference time. This approach involves estimating the biased representation using prompt-only querying and then removing it from the model’s internal representations. The results show that this approach can correct overfitted performance caused by prompt bias and significantly improve prompt retrieval capability (up to 10% absolute performance gain). These findings indicate that the proposed approach effectively alleviates prompt bias in knowledge evaluation, enhancing the reliability of benchmark assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models are biased when we ask them questions. It seems that no matter what kind of question we ask, the model will give us an answer that is influenced by the way we asked it. This is a problem because it means our models aren’t giving us accurate answers. The authors of this paper found that some types of questions are even more biased than others. They also discovered that when we test these language models, their scores can be artificially high just because of the way we ask them the questions. To fix this, they came up with a new way to remove this bias from the model’s answers. This approach worked really well and made the model give better answers. |
Keywords
» Artificial intelligence » Inference » Overfitting » Prompt
