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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30246
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dc.contributor.authorJenny, David F.-
dc.contributor.authorBilleter, Yann-
dc.contributor.authorSachan, Mrinmaya-
dc.contributor.authorSchölkopf, Bernhard-
dc.contributor.authorJin, Zhijing-
dc.date.accessioned2024-03-15T15:48:45Z-
dc.date.available2024-03-15T15:48:45Z-
dc.date.issued2023-11-15-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30246-
dc.description.abstractThe rapid advancement of Large Language Models (LLMs) has sparked intense debate regarding their ability to perceive and interpret complex socio-political landscapes. In this study, we undertake an exploration of decision-making processes and inherent biases within LLMs, exemplified by ChatGPT, specifically contextualizing our analysis within political debates. We aim not to critique or validate LLMs' values, but rather to discern how they interpret and adjudicate "good arguments." By applying Activity Dependency Networks (ADNs), we extract the LLMs' implicit criteria for such assessments and illustrate how normative values influence these perceptions. We discuss the consequences of our findings for human-AI alignment and bias mitigation.de_CH
dc.format.extent27de_CH
dc.language.isoende_CH
dc.publisherarXivde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectComputation and languagede_CH
dc.subjectArtificial intelligencede_CH
dc.subjectSocial networkde_CH
dc.subjectInformation networkde_CH
dc.subjectLarge language modelde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleNavigating the ocean of biases : political bias attribution in language models via causal structuresde_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.48550/arXiv.2311.08605de_CH
dc.identifier.doi10.21256/zhaw-30246-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedIntelligent Vision Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.relation.referenceshttps://github.com/david-jenny/LLM-Political-Studyde_CH
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Jenny, D. F., Billeter, Y., Sachan, M., Schölkopf, B., & Jin, Z. (2023). Navigating the ocean of biases : political bias attribution in language models via causal structures. arXiv. https://doi.org/10.48550/arXiv.2311.08605
Jenny, D.F. et al. (2023) Navigating the ocean of biases : political bias attribution in language models via causal structures. arXiv. Available at: https://doi.org/10.48550/arXiv.2311.08605.
D. F. Jenny, Y. Billeter, M. Sachan, B. Schölkopf, and Z. Jin, “Navigating the ocean of biases : political bias attribution in language models via causal structures,” arXiv, Nov. 2023. doi: 10.48550/arXiv.2311.08605.
JENNY, David F., Yann BILLETER, Mrinmaya SACHAN, Bernhard SCHÖLKOPF und Zhijing JIN, 2023. Navigating the ocean of biases : political bias attribution in language models via causal structures. arXiv
Jenny, David F., Yann Billeter, Mrinmaya Sachan, Bernhard Schölkopf, and Zhijing Jin. 2023. “Navigating the Ocean of Biases : Political Bias Attribution in Language Models via Causal Structures.” arXiv. https://doi.org/10.48550/arXiv.2311.08605.
Jenny, David F., et al. Navigating the Ocean of Biases : Political Bias Attribution in Language Models via Causal Structures. arXiv, 15 Nov. 2023, https://doi.org/10.48550/arXiv.2311.08605.


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