A new research paper titled "LLM-Ideoplasticity" proposes that the political ideology of large language models is not fixed but rather a conditional distribution influenced by context. The study evaluated nine LLMs using a framework that maps responses onto political dimensions, revealing significant sensitivity to factors like persuasive framing and under-represented languages. Despite this plasticity, the models collectively occupied a narrow ideological range compared to major political parties. AI
IMPACT This research suggests that understanding LLM behavior requires considering contextual factors, potentially impacting how AI systems are evaluated and deployed in sensitive areas like political discourse.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology and findings regarding LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- LLM
- LLM-Ideoplasticity
- ScienceCast
- Syed Rifat Raiyan
- VAA-CHES
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