Identifying intertextual parallels is central to philology, traditionally requiring labor-intensive manual analysis. While digitized historical corpora enable automated approaches using semantic sentence embeddings, training such models requires large annotated datasets, which are scarce for low-resource languages. We address this challenge by introducing a scalable automatic annotation pipeline for training semantic embedding models for Classical Tibetan. Our method combines unsupervised contrastive bootstrapping with iterative pair mining, generating silver-standard similarity labels through two complementary annotation strategies: (1) an ensemble of embedding models and rerankers, and (2) an LLM-as-a-judge committee using best–worst scaling. When combined with a domain-specific, gold-standard annotated dataset for sequential fine-tuning, the resulting text-similarity model achieves a state-of-the-art Spearman correlation of 0.864 on the STS task. This enables effective semantic search in Classical Tibetan and provides a framework for automatic supervision in low-resource languages used in digital humanities. We will make our code, dataset, and trained model publicly available upon publication.

We introduce a new computational framework for segmenting Classical Tibetan texts into autochthonous and allochthonous regions, distinguishing between indigenous Tibetan compositions and translated materials, primarily from Sanskrit sources. To support this task, we release the first annotated Tibetan corpus for ALLO/AUTO segmentation and evaluate several multilingual encoders, including mBERT and XLM-R, fine-tuned for sequence labeling. Our best model achieves strong alignment with expert annotations, showing that multilingual representations can effectively capture philological boundaries in low-resource settings. This work contributes new resources and methods for computational philology and sheds light on the linguistic markers that trace the intercultural transmission of Buddhist thought in Tibet.

We assess the capabilities of large language models on tasks involving Buddhist texts written in Sanskrit and Classical Tibetan—two typologically distinct, low-resource historical languages. To this end, we introduce DharmaBench, a benchmark suite comprising 13 classification and detection tasks grounded in Buddhist textual traditions: six in Sanskrit and seven in Tibetan, with four shared across both. The tasks are curated from scratch, tailored to the linguistic and cultural characteristics of each language. We evaluate a range of models, from proprietary systems like GPT-4o to smaller, domain-specific open-weight models, analyzing their performance across tasks and languages. All datasets and code are publicly released, under the CC-BY-4 License and the Apache-2.0 License respectively, to support research on historical language processing and the development of culturally inclusive NLP systems.