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Sanskrit language models can accelerate manuscript discovery, grammatical analysis, translation, and search across Vedic corpora. But a model that produces fluent Sanskrit is not necessarily reliable for philological research. It may normalise variant readings, mishandle sandhi, confuse grammatical analyses, or generate confident interpretations unsupported by the source text.

This guide explains how to benchmark Sanskrit language models for Vedic research in a way that is measurable, reproducible, and useful to scholars and builders in India. The central principle is simple: evaluate models on the actual research tasks they will perform, not only on generic language-model scores.

Define the research task before choosing a metric

“Vedic research” covers several distinct tasks. Write a task specification before preparing data or selecting a model. A useful specification records the input, expected output, evidence available to the model, and what counts as an acceptable error.

Common tasks include:

• Text retrieval: Find relevant hymns, passages, pada readings, commentaries, or parallel occurrences.
• Transliteration and normalisation: Convert between Devanagari, IAST, Harvard-Kyoto, SLP1, and other schemes without losing distinctions.
• Morphological analysis: Identify lemma, stem, case, number, gender, tense, mood, person, and derivational information.
• Sandhi splitting and segmentation: Recover word boundaries while preserving alternative analyses.
• Dependency parsing: Represent syntactic relations in a verse or prose passage.
• Translation: Produce a target-language rendering that remains faithful to grammar and context.
• Question answering and summarisation: Answer research questions with passage-level citations rather than unsupported explanations.
• Variant comparison: Align editions, readings, manuscripts, and editorial notes.

For projects working with limited annotated data, the principles in this guide complement a broader builder’s guide to low-resource Indic NLP. Do not combine all tasks into one score: a model can excel at transliteration and fail at Vedic syntax.

Build a leakage-resistant evaluation corpus

Dataset quality often matters more than model size. Assemble separate training, development, and test sets, and document the provenance of every item. The test set should be locked before model tuning begins.

A robust Vedic benchmark should capture:

• Textual diversity: Saṃhitā, Brāhmaṇa, Āraṇyaka, Upaniṣadic, Vedāṅga, and commentary material where relevant to the project.
• Editorial diversity: Multiple editions, recensions, scripts, transliteration systems, and orthographic conventions.
• Linguistic difficulty: Sandhi-heavy passages, irregular forms, compounds, archaic vocabulary, accent marks, and ambiguous parses.
• Research metadata: Text title, śākhā or recension, passage identifier, edition, page or verse reference, and annotation provenance.
• Human judgements: At least two qualified annotators for difficult analyses, with adjudication notes for disagreements.

Avoid random line-level splitting when the same hymn, commentary, or formula appears across partitions. Near-duplicate passages can make a model appear strong through memorisation. Split by text, work, recension, or source family, depending on the leakage risk. Maintain a “challenge set” containing rare forms and examples deliberately excluded from training.

For new corpora, inspect the low-resource language datasets available for AI training in India, but verify licensing, annotation quality, and Vedic relevance before incorporating any resource.

Use task-specific metrics—not one headline number

Text prediction and language modelling

Perplexity is useful for comparing models under identical tokenisation and test data, but it is not a measure of philological correctness. Report the tokenizer, vocabulary, sequence length, script, normalisation rules, and whether test passages may have appeared in pretraining. Add character- or morpheme-level analyses where wordpiece tokenisation fragments Sanskrit compounds excessively.

Morphology, segmentation, and parsing

Use exact match, precision, recall, and F1 for morphological tags and sandhi splits. For segmentation, report boundary F1 and complete-sentence accuracy. For dependency parsing, report labelled and unlabelled attachment scores, while separately analysing compounds and non-projective structures.

Exact match is especially important in research workflows: a sentence with one incorrect case or dependency may lead to a different interpretation even when its overall F1 is high.

Translation and explanation

BLEU and chrF can support automated comparison, but they should not be treated as final evidence. Sanskrit allows multiple valid translations, and reference translations may encode one interpretive choice. Add expert ratings for:

• grammatical fidelity;
• preservation of ambiguity;
• treatment of technical terms and names;
• consistency with the cited passage; and
• unsupported additions or omissions.

For generated explanations, score citation precision: does each claim follow from the cited text, and does the citation identify the correct passage? Track abstention quality as well. A model that says “the evidence is insufficient” should score better than one that invents a reading.

Retrieval and question answering

Use recall@k, precision@k, mean reciprocal rank, and nDCG for passage retrieval. Build questions that require locating evidence, not merely recalling famous verses. For answer generation, report retrieval quality separately from answer quality; otherwise a fluent answer can conceal a failed search step.

Create a reproducible benchmark harness

A credible evaluation should be rerunnable by another researcher. Record:

• model name, version, checkpoint, and parameter count;
• prompt templates and system instructions;
• decoding settings, random seeds, and number of runs;
• tokenizer and script-normalisation pipeline;
• hardware, software versions, and inference precision;
• training data or fine-tuning sources, where disclosed; and
• every evaluation output, including failures and abstentions.

Use deterministic decoding for extraction and structured prediction. For open-ended translation or interpretation, run multiple seeds and report mean scores with variation. Store predictions in a machine-readable format containing the input identifier, output, evidence, confidence, and error label.

A lightweight benchmark repository should include a data card, annotation guidelines, evaluation scripts, baseline results, and a clear licence. If the project will become a public research tool, plan the workflow alongside an AI research assistant tool architecture, particularly for citation tracking and human review.

Evaluate human usefulness and failure modes

Automated metrics cannot determine whether a model is safe for Vedic scholarship. Conduct a blinded expert review using a stratified sample of easy, routine, and adversarial cases. Ask reviewers to label:

• factual or grammatical errors;
• fabricated citations;
• modern meanings imposed on archaic usage;
• collapse of legitimate ambiguity;
• incorrect handling of accents, compounds, or sandhi;
• copied or memorised passages presented as analysis; and
• whether the output saves time without replacing verification.

Report errors by category, text genre, script, and difficulty. Include examples in the benchmark report. Builders should also measure latency, memory use, cost per 1,000 items, and performance on local hardware—important constraints for Indian universities and archives.

Compare baselines fairly

Start with transparent baselines: dictionary lookup, rule-based sandhi splitting, a classical Sanskrit parser, a multilingual model, and a Sanskrit-specialised model. Keep the test set and preprocessing fixed across systems. Compare zero-shot, few-shot, retrieval-augmented, and fine-tuned settings separately.

Do not claim that a larger model is better because it generates more polished prose. A smaller model with reliable retrieval, structured outputs, and strong abstention may be more useful in a scholarly workflow. For deployment decisions, consider the same practical trade-offs used when fine-tuning Llama for Indian regional languages: data quality, domain fit, inference cost, licence, and maintenance burden.

A practical reporting checklist

Before publishing results, confirm that you have:

• defined the Vedic tasks and error costs;
• separated texts and recensions to prevent leakage;
• documented scripts, tokenisation, and normalisation;
• reported task-specific metrics and confidence intervals where possible;
• included expert evaluation and representative failures;
• tested citation grounding and abstention;
• disclosed model and data limitations; and
• released evaluation code or a reproducible alternative.

The strongest Sanskrit benchmark is not the one with the most metrics. It is the one that makes a model’s capabilities, blind spots, and research usefulness visible. For Indian teams building language technology, that means treating textual scholarship, dataset governance, and engineering reproducibility as one evaluation problem—not separate afterthoughts.