Summary of Quantifying Positional Biases in Text Embedding Models, by Samarth Goel et al.
Quantifying Positional Biases in Text Embedding Models
by Samarth Goel, Reagan J. Lee, Kannan Ramchandran
First submitted to arxiv on: 13 Dec 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 The paper investigates the impact of content position and input size on text embeddings, revealing a bias towards prioritizing the beginning of an input. The study shows that this bias can lead to a 12.3% reduction in cosine similarity between altered and original embeddings when irrelevant text is inserted at the start of a document. Regression analysis also confirms this trend, with sentence importance declining as position moves further from the start. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well language models do when they’re asked to understand longer pieces of text. It finds that these models tend to get it wrong if the first part of the text is different from what’s expected. For example, if you add some random words to the beginning of an article, the model might not recognize it as the same article anymore. The study also shows that this problem gets worse as the text gets longer. |
Keywords
» Artificial intelligence » Cosine similarity » Regression
