Summary of Self-supervised Position Debiasing For Large Language Models, by Zhongkun Liu et al.
Self-Supervised Position Debiasing for Large Language Models
by Zhongkun Liu, Zheng Chen, Mengqi Zhang, Zhaochun Ren, Pengjie Ren, Zhumin Chen
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 self-supervised position debiasing (SOD) framework aims to mitigate position bias in large language models (LLMs), which can lead to poor generation performance. Existing methods require external knowledge or annotated non-biased samples, but SOD leverages unsupervised responses from pre-trained LLMs without relying on any external information. The SOD framework consists of two components: an objective alignment (OAM) module to prune the unsupervised responses and improve their quality. Experimental results on eight datasets and five tasks demonstrate that SOD consistently outperforms existing methods in mitigating three types of position biases, sacrificing only a small performance on biased samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be tricked into giving poor answers by fine-tuning them on specific data. This is because they often pick up shortcuts or biases in the training data that don’t actually help with the task at hand. The problem is that existing ways to fix this require a lot of extra information, like annotated samples or special knowledge about what’s biased and what’s not. But what if we could just use the model itself to figure out what it’s doing wrong? That’s the idea behind a new technique called self-supervised position debiasing (SOD). SOD takes an existing large language model and uses its own responses to identify and fix the biases. It’s like training a detective to solve a mystery without giving them any clues. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Large language model * Self supervised * Unsupervised
