Testimony by LLMs

Abstract Artificial testimony generated by large language models (LLMs) can be a source of knowledge. However, the requirement that artificial testifiers must satisfy for successful knowledge acquisition is different from the requirement that human testifiers must satisfy. Correspondingly, the epistemic ground of artificial testimonial knowledge is not the well-known and accepted ones suggested by renowned epistemological theories of (human) testimony. Based on Thomas Reid’s old teaching, we suggest a novel epistemological theory of artificial testimony that for receivers to justifiably believe artificially generated statements, testifiers of the statement should robustly perform the propensities of veracity and cautiousness. The theory transforms the weakness of Reid’s view to an advantage of its own. It sets an achievable standard for LLMs and clarifies the improvement that current LLMs should make for meeting the standard. Moreover, it indicates a pluralistic nature of testimonial justification pertaining to the pluralistic nature of possible testifiers for knowledge transmission.