Summary of Large Language Models Are Biased Because They Are Large Language Models, by Philip Resnik
Large Language Models are Biased Because They Are Large Language Models
by Philip Resnik
First submitted to arxiv on: 19 Jun 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 This position paper aims to spark discussion on the connection between bias and fundamental properties of large language models. It argues that harmful biases are an inherent consequence of current LLM designs, which necessitates a reevaluation of AI driven by LLMs, questioning the foundational assumptions underlying their design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have a problem with bias, and this paper says it’s because of how they’re designed. Essentially, making these models work requires certain assumptions about what makes good language, but that can lead to harmful biases. So, we need to rethink AI and large language models from the ground up. |
