IMPORTANT NOTE: This article is not about faith, religion or church. An English version of the Bible (any single translation) is used simply as a context…a use case. The conclusions are the same: AI will always lie.
Conclusion: Yes — almost all of it is still true.
Using only one single translation (e.g., just NIV or just NRSV) removes inter-translation disagreement, but it does not eliminate knowable lies for ordinary users.
What no longer applies (or is greatly reduced)
Inter-translation conflict
If the model is trained on only one translation:
- It can’t contradict another English translation
- It can’t blend wording across versions
So this specific failure mode disappears:
“The Bible says X” when another translation clearly says Y
But this is a narrow improvement.
What still applies (and why knowable lies remain)
1. A single translation is still not ground truth
Even one translation:
- encodes interpretive decisions
- smooths ambiguity in the source languages
- chooses one meaning where multiple exist
So the model may assert:
“The text means X”
when the underlying text reasonably allows ¬X — a knowable falsehood once checked against scholarship.
2. Generalization beyond the text still occurs
LLMs:
- extrapolate patterns
- infer doctrines
- merge nearby passages
This produces statements that:
- are not stated anywhere
- sound consistent with the text
- are verifiably unsupported
→ knowable lies remain possible.
3. Coverage gaps are unavoidable
A Bible-only model still lacks:
- modern history
- natural science
- detailed biographies
- post-biblical theology
Questions outside the text’s scope force:
- refusal, or
- invention
Without strict refusal logic, invention = knowable lie.
4. Reasoning errors are corpus-independent
Logical errors arise from:
- probabilistic prediction
- lack of formal validation
- rhetorical coherence bias
A conclusion can be false even if every quoted verse is accurate.
5. Overconfidence remains unless designed out
A single-translation model will still:
- state interpretations as facts
- omit uncertainty markers
- collapse metaphor into literal claims
Users can verify the mistake → knowable lie.
Key takeaway (the deep point)
Knowable lies emerge from inference, not disagreement.
Removing alternative translations:
- improves consistency
- does not guarantee epistemic correctness
A model trained on one perfect, immutable text can still confidently assert false claims about that text.
Bottom line
SetupKnowable lies possible?Why4 translationsYesAmbiguity + inference4 translations, fixed versionsYesInference1 translation only****YesInference + scope limits