Summary of Bias in Llms As Annotators: the Effect Of Party Cues on Labelling Decision by Large Language Models, By Sebastian Vallejo Vera and Hunter Driggers
Bias in LLMs as Annotators: The Effect of Party Cues on Labelling Decision by Large Language Models
by Sebastian Vallejo Vera, Hunter Driggers
First submitted to arxiv on: 28 Aug 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 As machine learning educators write for a technical audience not specialized in this subfield, we find that Large Language Models (LLMs) replicate human biases as annotators. By replicating an experiment from Ennser-Jedenastik and Meyer (2018), our findings show LLMs use political information to contextualize whether statements are positive, negative, or neutral based on party cues. Furthermore, we observe that LLMs reflect the biases of their human-generated training data, unlike humans who only exhibit bias when faced with extreme parties. The implications of these results are discussed in the conclusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) can be biased too! Just like humans, they can use political information to decide if something is good or bad. But here’s the thing: LLMs don’t just use that info when it comes from super extreme parties. They’re biased even when the statements are from more moderate parties. This matters because we want AI systems to be fair and not pick favorites. |
Keywords
* Artificial intelligence * Machine learning
