The Complex Art of Translating Ancient Wisdom: LLMs Put to the Test

Translating ancient texts is as much an art as it's a science, particularly when dealing with classical languages like Pali. Recent experiments involving four prominent large language models (LLMs) shed light on how well these tools handle the Pali-to-English translation challenge. By dissecting the performance of GPT-5.5, Claude Sonnet 4.6, Gemini 3.1 Pro, and Grok 4.3 on 1,700 passages from the Pali Canon, researchers paint a nuanced picture of modern translation technology's capabilities and shortcomings.

The Audit Approach

The audit didn't rely on a single 'correct' translation. Instead, it used a reference envelope from three respected human translations by Bhikkhu Sujato, Thanissaro Bhikkhu, and Bhikkhu Bodhi. This method acknowledges the legitimate variation inherent in translating classical texts. The process examined how much each model's output drifted from this reference centroid. A drift threshold of 1.5 was set to triage which outputs needed closer human adjudication.

Findings That Stand Out

Two striking results emerged from this in-depth analysis. First, the concept of 'drift' relates more to the severity of potential errors than to errors themselves. For instance, candidates with a drift above 3.0 showed a major-error rate climbing to 51.6%, while those in the 1.5-2.0 range were often valid variations. Second, when comparing models, GPT-5.5 exhibited the lowest high-drift major-error rate, though its confidence intervals were similar to those of Claude Sonnet 4.6 and Gemini 3.1 Pro. On the other hand, Grok 4.3 was the most prone to outliers, suffering from both the largest volume and highest error rate particularly above a drift of 3.0.

Implications for Translation

The major-error categories, omissions, truncations, doctrinal term misinterpretations, are the very issues likely to confuse or mislead those studying doctrinal texts. This isn't just an academic exercise. it's about ensuring fidelity in translation that could impact understanding on a global scale. One must ask, are businesses and scholars prepared for the real cost of relying on AI models in sensitive areas like religious studies? The consulting deck might promise smooth integration, but the P&L might tell a different story.

At the heart of this study is a reusable audit framework that could redefine translation standards. It moves beyond treating outliers as errors, suggesting instead a systemized approach to prioritize reviews. Here's what the deployment actually looks like: define your envelope with multiple human translations, use embedding drift for triage, and focus adjudication efforts on flagged results. The ROI case requires specifics, not slogans, and this model offers a roadmap to getting it right.