The push for linguistic inclusivity in Artificial Intelligence has brought Low-Resource Languages (LRLs) to the forefront of research. In the Indian context, Telugu—a Dravidian language spoken by over 80 million people—and Sanskrit—the classical liturgical language of Hinduism and a cornerstone of Indo-Aryan linguistics—present unique challenges. Benchmarking NLP models for Telugu and Sanskrit is no longer just an academic exercise; it is a prerequisite for building functional Indic AI, from government chatbots to cultural archiving tools.
Unlike English, which benefits from massive web-scraped datasets and standardized evaluation frameworks like GLUE or SuperGLUE, Telugu and Sanskrit require specialized approaches. This article explores the current state of benchmarking, the architectural hurdles, and the specific datasets driving NLP progress for these two distinct yet historically intertwined languages.
The Linguistic Landscape: Why Benchmarking is Difficult
To understand why benchmarking NLP models for Telugu and Sanskrit is complex, one must look at their morphological structures:
- Telugu (Agglutinative): Telugu is highly agglutinative. A single word can contain multiple morphemes, often equivalent to an entire sentence in English. Models must handle complex "Sandhi" (word-merging) and "Samasa" (compounding) rules.
- Sanskrit (Inflectional & Free Word Order): Sanskrit is a highly inflected language with a complex system of cases (vibhakti). Furthermore, it allows for flexible word order, making syntactic dependency parsing significantly harder for standard Transformer architectures that rely heavily on positional encodings.
Traditional benchmarks often fail because they do not account for these nuances, leading to high "Out-of-Vocabulary" (OOV) rates and poor semantic understanding.
Key Datasets for Benchmarking Telugu and Sanskrit
A benchmark is only as good as its data. For Indic languages, the ecosystem has evolved from generic Wikipedia scrapes to curated subsets.
1. IndicGLUE and Airavata
IndicGLUE, part of the AI4Bharat initiative, is the most prominent benchmark for Telugu. It includes tasks like:
- Sentiment Analysis: Classification of Telugu movie reviews or social media posts.
- Named Entity Recognition (NER): Identifying locations, people, and organizations in Telugu script.
- Article Classification: Categorizing news into blocks like sports, business, and politics.
2. SanskritShala and Digital Sanskrit Corpus
For Sanskrit, the benchmarking focus is often on traditional linguistic tasks rather than modern sentiment.
- Word Segmentation: Breaking down complex Sandhi strings.
- Morphological Parsing: Identifying the root, gender, case, and number.
- Dependency Parsing: Understanding the relationship between words in a shloka.
Cross-Lingual Transfer vs. Native Training
When benchmarking NLP models for Telugu and Sanskrit, a major point of comparison is whether to use a Multilingual Model or a Monolingual Model.
- mBERT and XLM-R: These models are often used as baselines. However, Telugu is frequently under-represented in their training corpuses, leading to "tokenization tax" where a single word is split into too many sub-word units, losing semantic meaning.
- IndicBERT (AI4Bharat): This model specifically targets 11+ Indian languages. It consistently outperforms mBERT on Telugu benchmarks because its tokenizer is trained on Indian scripts, allowing for more efficient representation.
- Specialized Sanskrit Models: Models like *Sanskrit-BERT* or those using the *Dharani* framework focus on the Devanagari script's nuances, particularly the importance of phonetic structure in classical texts.
Technical Benchmarking Metrics
Standard accuracy metrics (F1-score, Exact Match) are used, but high-quality benchmarking for these languages requires additional metrics:
1. ChrF++: Often superior to BLEU for Telugu because it operates at the character n-gram level, making it better at evaluating morphologically rich, agglutinative languages.
2. WER (Word Error Rate): Crucial for Automatic Speech Recognition (ASR) in Telugu, where colloquial variations differ significantly from formal written script.
3. ROUGE-L: Used for summarization tasks, though it struggles with Sanskrit's free word order unless combined with semantic similarity measures.
The Role of LLMs: Llama-3 and Beyond
With the advent of Large Language Models (LLMs), benchmarking has shifted toward generative capabilities.
- Instruction Fine-Tuning: Benchmarking how well models like Llama-3 or Mistral follow instructions in Telugu. Projects like *Telugu Llama* have shown that LoRA (Low-Rank Adaptation) fine-tuning on Telugu-specific instruction sets significantly boosts performance over base multilingual models.
- Zero-Shot Sanskrit Reasoning: Researchers are currently benchmarking GPT-4 and Claude on their ability to translate and interpret ancient Sanskrit texts. While GPT-4 shows high fluency, it often "hallucinates" grammatical rules that don't exist in Paninian grammar, highlighting the need for specialized Sanskrit benchmarks.
Challenges in Crowdsourcing and Validation
A major bottleneck in benchmarking NLP models for Telugu and Sanskrit is the lack of "Gold Standard" human-annotated data.
- Telugu: Most datasets are sourced from news articles, which do not represent the dialectal diversity of Andhra Pradesh and Telangana.
- Sanskrit: High-quality annotation requires scholars fluent in *Vyakarana* (grammar). This makes the creation of evaluation sets expensive and slow.
Future Directions: Moving Toward Holistic Benchmarks
To achieve parity with English NLP, the community must focus on:
- Long-Context Benchmarks: Testing how models handle long Telugu narratives or entire Sanskrit chapters.
- Code-Switching: Telugu speakers frequently mix English words (Tanglish). Benchmarking must include "code-mixed" datasets to be relevant for modern applications.
- Phonetic Benchmarks: Leveraging the phonetic nature of Devanagari and Telugu scripts to improve text-to-speech (TTS) and speech-to-text (STT) models.
Conclusion
Benchmarking NLP models for Telugu and Sanskrit is a journey from generic multilingualism to localized linguistic precision. While Telugu benefits from large-scale government and digital initiatives, Sanskrit requires a deep-tech approach that respects its classical structure. For developers and researchers in India, leveraging benchmarks like IndicGLUE while contributing to open-source datasets is the only way to ensure that AI truly speaks the language of the people.
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FAQ: Benchmarking Indic NLP
Q1: What is the best model for Telugu NLP currently?
For standard tasks, IndicBERT v2 or fine-tuned versions of Llama-3 (Telugu-Llama) offer the best balance of performance and efficiency.
Q2: Can I use English benchmarks for Sanskrit?
No. English benchmarks rely on word order and simple morphology. Sanskrit requires benchmarks that evaluate morphological parsing and Sandhi splitting.
Q3: Where can I find datasets for Telugu benchmarking?
The AI4Bharat portal and the Linguistic Data Consortium for Indian Languages (LDC-IL) are the primary sources for high-quality Telugu datasets.
Q4: Why is tokenization important for these languages?
Standard Western tokenizers often break Telugu or Sanskrit words into meaningless fragments. Benchmarking involves testing different tokenizers to see which preserves the most semantic information per token.