Research Overview
This study examines the critical role of human agency in AI-assisted translation, focusing specifically on how the deliberate selection of reference databases significantly influences translation outcomes for Classical Chinese Buddhist texts. As large language models (LLMs) become increasingly accessible to scholars through localized implementations, this research demonstrates how human judgment remains essential in specialized religious translation despite technological advances.
Technological Framework and Scope
The research implements Retrieval-Augmented Generation (RAG) through RAGFlow, an open-source compact RAG engine that enhances LLM performance by integrating traditional information retrieval systems. The Deepseek R1 model was selected for its superior performance in localized computing environments, allowing for controlled testing under typical scholarly resource constraints.
Unlike traditional neural machine translation (NMT), which often struggles with specialized terminology, the RAG approach enables selective incorporation of domain-specific knowledge sources. This methodological choice is particularly significant for Buddhist texts, where theological concepts require contextual understanding beyond general language processing capabilities.
The study focuses on the 25th chapter of the Fahuajing, the Pumenpin, one of the most significant texts in Mahayana Buddhism. This text was selected not to generate a new translation but to identify which combinations of embedded reference data can produce optimal results. The Pumenpin is ideal for this methodology because it has multiple existing translations and extensive commentary resources, offering clear benchmarks for evaluation and rich data sources for testing different RAG configurations.
Working with a well-documented text like the Pumenpin allows the research to focus on methodological innovation, integrating RAG with local LLMs for specialized translation tasks rather than dealing with uncertainties in textual interpretation. This approach enables clear identification of how human decisions in selecting reference materials affect translation outcomes despite using identical AI technology.
Research Methodology and Procedures
The research process begins with a baseline translation using the Deepseek R1 model without specialized reference materials. Multiple translation iterations will then be conducted using identical AI settings but varying the reference databases provided to the RAG system. These databases include general Buddhist dictionaries providing broad terminological coverage, sectarian commentaries from different Buddhist traditions, historical commentaries from different time periods, and combined specialized sources reflecting particular doctrinal lineages.
For each iteration, the translation process will document how terminological choices, structural interpretations, and doctrinal emphases appear to be influenced by the specific reference materials used. Particular attention will be paid to key Buddhist terms, doctrinal concepts, and passages that have historically been subject to different interpretations across traditions.
The evaluation process leverages the existence of multiple authoritative translations as standards against which to measure the AI-generated translations. These established translations serve as benchmarks for assessing accuracy and appropriateness in terms of both word choice and theological understanding. The evaluation will include two major components. The first component assesses potential issues common in machine translation of religious texts, including mistranslation of specialized terminology, loss of doctrinal nuance, inappropriate modernization of traditional concepts, semantic drift in key theological terms, and the generation of plausible-sounding but doctrinally incorrect interpretations. The second component is a doctrinal identification analysis that examines how different reference materials guide the AI toward particular sectarian interpretations or doctrinal positions that reflect the theological orientation of the source materials. This comprehensive evaluation framework helps determine which configurations of the RAG system produce translations that most closely align with established scholarly standards while identifying how the choice of reference materials influences interpretive outcomes.
Theoretical Implications
This research aims to contribute to discussions on AI agency by examining how AI-assisted translation might amplify rather than diminish the importance of human expertise in religious text translation. The study shows that the translator's role may shift from word-by-word rendering to making higher-order decisions about knowledge sources that fundamentally shape the resulting translation.
The research seeks to demonstrate that in specialized religious contexts, AI systems do not replace human interpretive judgment but rather engage with it in different ways. Human expertise will be critical in addressing the limitations of AI agency, including its hallucinations and its challenges in managing specialized knowledge.
Conclusion
This research establishes a methodological framework for AI-assisted translation of religious texts that maintains human interpretive control. Rather than diminishing human expertise, AI assistance requires deliberate engagement from the beginning of the translation process. The translator's selection of reference materials creates an interpretive framework that shapes all subsequent outcomes, demonstrating how human judgment remains essential even as translation becomes increasingly AI-assisted.
Utilizing machine translation enhances translation efficiency and academic communication, potentially inspiring new interpretations of classical texts. However, the results heavily depend on corpus selection, requiring translators to exercise sufficient judgment to avoid being influenced by seemingly reasonable but ultimately misleading outputs. This balance between technological assistance and human discernment represents a new paradigm in religious text translation that preserves theological integrity while leveraging computational advantages.
This research establishes a methodological framework for AI-assisted translation of specialized religious texts. The framework recognizes the continued centrality of human expertise in the translation process and provides concrete strategies for scholars to leverage AI tools while maintaining interpretive control. It demonstrates how conscious selection of reference materials allows for alignment with specific doctrinal traditions and establishes evaluation criteria for assessing the theological coherence of AI-assisted translations.
This study examines the role of human agency in AI-assisted translation, focusing on how the selection of reference databases influences the translation of Classical Chinese Buddhist texts. Using Retrieval-Augmented Generation (RAG) with the Deepseek R1 model, the research evaluates how consciously selected knowledge sources impact translation accuracy and doctrinal interpretation. The study applies this methodology to the Pumenpin chapter of the Fahuajing, analyzing multiple translation iterations with varying reference materials. Evaluation is conducted against authoritative translations to assess accuracy, doctrinal nuance, and interpretive biases. The study demonstrates that AI does not replace human expertise but instead requires active engagement in selecting reference sources, which fundamentally shape translation outcomes. This research establishes a methodological framework for AI-assisted religious text translation, emphasizing the necessity of human oversight to maintain theological coherence while leveraging computational advancements. It highlights the evolving role of translators in curating AI inputs rather than merely post-editing outputs.