Summary of Capturing Bias Diversity in Llms, by Purva Prasad Gosavi and Vaishnavi Murlidhar Kulkarni and Alan F. Smeaton
Capturing Bias Diversity in LLMs
by Purva Prasad Gosavi, Vaishnavi Murlidhar Kulkarni, Alan F. Smeaton
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study enhances Large Language Models (LLMs) by injecting diversity into their generated outputs. This is achieved by developing multiple customized instances of a GPT model, each reflecting biases in specific demographic characteristics such as gender, age, and race. The framework, dubbed BiasGPT, enables the collaboration of these models to merge their diverse perspectives on a topic, resulting in an integrated response that captures a broad spectrum of human experiences and viewpoints. This research demonstrates the capabilities of GPT models to embed different biases, which can ultimately lead to more inclusive AI technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to improve Large Language Models by adding diversity to their generated outputs. Scientists created multiple versions of a special kind of language model called GPT. Each version was designed to reflect specific characteristics like gender, age, or race. By combining these different models, researchers hope to create more inclusive AI technologies that can understand and respond to people’s perspectives in a more nuanced way. |
Keywords
» Artificial intelligence » Gpt » Language model
