  I report here a comprehensive analysis about the political preferences
embedded in Large Language Models (LLMs). Namely, I administer 11 political
orientation tests, designed to identify the political preferences of the test
taker, to 24 state-of-the-art conversational LLMs, both closed and open source.
When probed with questions/statements with political connotations, most
conversational LLMs tend to generate responses that are diagnosed by most
political test instruments as manifesting preferences for left-of-center
viewpoints. This does not appear to be the case for five additional base (i.e.
foundation) models upon which LLMs optimized for conversation with humans are
built. However, the weak performance of the base models at coherently answering
the tests' questions makes this subset of results inconclusive. Finally, I
demonstrate that LLMs can be steered towards specific locations in the
political spectrum through Supervised Fine-Tuning (SFT) with only modest
amounts of politically aligned data, suggesting SFT's potential to embed
political orientation in LLMs. With LLMs beginning to partially displace
traditional information sources like search engines and Wikipedia, the societal
implications of political biases embedded in LLMs are substantial.
