Domain Adaptation and Reasoning Frameworks in Language Models: A Controlled Experiment with Historical Cosmology
arXiv:2605.30415v1 Announce Type: cross Abstract: We investigate how domain adaptation reshapes explanatory behavior in language models using historical cosmology as a controlled setting. In Phase 1, we train a small language model from scratch on a pre-Copernican corpus from which explicit heliocentric references were removed, and evaluate whether Earth-motion or heliocentric continuations nevertheless emerge. In Phase 2, we fine-tune a larger pretrained model using QLoRA on the same corpus in order to study how adaptation modifies explanatory framing and cosmological stance. Model outputs ar
The proliferation of advanced language models necessitates deeper understanding of their learning mechanisms and potential biases, particularly when adapting to specific domains.
This research provides insights into how domain adaptation influences AI's explanatory behavior and 'stance', crucial for building unbiased and reliable AI systems across various applications.
Our understanding of how fine-tuning impacts large language models' fundamental interpretive and reasoning frameworks could shift, leading to more robust and controlled AI development.
- · AI researchers
- · Ethical AI developers
- · Domain-specific AI applications
- · Developers of uncritical AI fine-tuning methods
- · Sectors relying on potentially biased AI explanations
Improved methods for domain adaptation in AI will emerge, reducing the risk of unintended biases or shifts in explanatory framing.
Enhanced control over AI's 'worldview' could accelerate its deployment in sensitive areas requiring specific interpretive stances, such as legal or medical fields.
The ability to deliberately shape AI's foundational reasoning might lead to a new form of 'AI education' where models are trained not just on data but on specific philosophical or scientific frameworks.
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Read at arXiv cs.AI