Summary of Mitigate Position Bias in Large Language Models Via Scaling a Single Dimension, by Yijiong Yu et al.
Mitigate Position Bias in Large Language Models via Scaling a Single Dimension
by Yijiong Yu, Huiqiang Jiang, Xufang Luo, Qianhui Wu, Chin-Yew Lin, Dongsheng Li, Yuqing Yang, Yongfeng Huang, Lili Qiu
First submitted to arxiv on: 4 Jun 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 paper explores the phenomenon of position bias in Large Language Models (LLMs), where the placement of key information in a prompt can significantly affect accuracy. It identifies that attention weights and causal attention masks contribute to this bias, leading to varying performance across different positions. To mitigate this issue, the authors propose a method that scales positional hidden states, demonstrating its effectiveness on various tasks using different models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like text. But did you know that where you put certain information in the text can make it more or less accurate? This paper looks into this problem called position bias, which means that what’s at the beginning, middle, or end of a sentence matters for how well the model performs. The scientists found out that two special techniques used by the models make this problem worse. They then came up with a new way to fix it and showed that it works really well on different tasks like answering questions and finding information in text. |
Keywords
» Artificial intelligence » Attention » Prompt
