Summary of On Debiasing Text Embeddings Through Context Injection, by Thomas Uriot
On Debiasing Text Embeddings Through Context Injection
by Thomas Uriot
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 reviews 19 embedding models in Natural Language Processing (NLP) and quantifies their biases and responses to context injection for debiasing. The authors find that higher-performing models are more prone to capturing biases, but also better at incorporating context. They also identify a surprising limitation: while models can embed affirmative semantics easily, they struggle with neutral semantics. To address this issue, the authors design a simple algorithm for top-k retrieval, which dynamically selects k based on relevance. The algorithm demonstrates improved performance in retrieving gendered and neutral chunks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how language processing models represent text into vectors, called embeddings. These embeddings can carry biases from the text data they’re trained on. Researchers have tried to fix this by adding context, but it’s not clear which embedding models are best for this. The authors look at 19 different models and see how well they capture biases and respond to added context. They find that better models are more likely to catch biases, but can also handle context better. Another surprise is that these models have trouble with neutral text – it’s easy to understand “good” or “bad”, but not so much “okay”. The authors use this knowledge to create a new way of finding relevant parts of text called top-k retrieval. |
Keywords
» Artificial intelligence » Embedding » Natural language processing » Nlp » Semantics
