Summary of Eliminating Position Bias Of Language Models: a Mechanistic Approach, by Ziqi Wang et al.
Eliminating Position Bias of Language Models: A Mechanistic Approach
by Ziqi Wang, Hanlin Zhang, Xiner Li, Kuan-Hao Huang, Chi Han, Shuiwang Ji, Sham M. Kakade, Hao Peng, Heng Ji
First submitted to arxiv on: 1 Jul 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 A mechanistic analysis reveals that position bias in modern language models (LMs) stems from two key components: causal attention and relative positional encodings. To address this issue, a training-free zero-shot approach is proposed, which eliminates position bias by replacing causal attention with bidirectional attention between documents. This method, known as Position-INvariant inferencE (PINE), uses model attention values to determine the relative orders of documents instead of relying on input prompt order. By eliminating position bias, LMs achieve better performance and reliability in downstream tasks such as LM-as-a-judge, retrieval-augmented QA, molecule generation, and math reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how language models can be improved by getting rid of a common problem called position bias. This happens when the model favors certain information just because it appears at the beginning or end of what’s being read. The researchers found that two key parts of the model are causing this issue and came up with a simple solution to fix it. By changing how the model pays attention to different parts of the text, they were able to make the model more accurate and reliable. |
Keywords
» Artificial intelligence » Attention » Inference » Prompt » Zero shot
