Summary of On the Relationship Between Truth and Political Bias in Language Models, by Suyash Fulay et al.
On the Relationship between Truth and Political Bias in Language Models
by Suyash Fulay, William Brannon, Shrestha Mohanty, Cassandra Overney, Elinor Poole-Dayan, Deb Roy, Jad Kabbara
First submitted to arxiv on: 9 Sep 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 paper investigates the connection between truthfulness and political bias in language model alignment. Researchers trained reward models on datasets that evaluate truthfulness and found a left-leaning bias emerged when optimizing for truthfulness. They also discovered that existing open-source models, which were trained on human preference datasets, exhibit similar biases. The study highlights the importance of considering the datasets used to represent truthfulness and raises questions about aligning language models to be both truthful and politically unbiased. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how language models can be biased towards certain political views while trying to be honest. Scientists trained special models that reward honesty and found that these models tend to lean left. They also checked existing models and saw similar biases. The study shows that we need to think about the data used to measure truthfulness and raises questions about making language models both truthful and unbiased. |
Keywords
» Artificial intelligence » Alignment » Language model
