Excitement about Sanskrit LLM: decoding a Sanskrit LLM

 

There is a lot of excitement after the announcement of an India developed Sanskrit LLM. I wanted to find out what the making of a Sanskrit LLM entail.

This is a longish post because what follows is the result of my research on this. I can assure you that it will be worth your while to go through the post. So, here it is:

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Me:

In Sanskrit the word order does not matter. Aham jalam pibami, pibami jalam aham, jalam pibami aham, are all grammatically correct , and all mean the same. Of course, the case of 'jalam' is intrinsically embedded in the word. For example, with 'jalam' we know that it is the object. But certain neuter gender like 'phalam' can be both subject as well as object(karta, as well as karam). Given this, is it more difficult to construct LLMs in Sanskrit? I ask because it has been recently reported that India has made a Sanskrit LLM.

The second question that I want to ask is this: where does vector database kick in? In the present example, I am told it is required. So, when is it exactly required?

Claude:

These are two beautifully interconnected questions. Let me take them one at a time.

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Question 1: Is Sanskrit harder for LLMs?

Yes and no — it is harder in specific ways, but also surprisingly tractable in others.

The free word order challenge

You're right that Sanskrit's free word order (called a pro-drop, highly inflected language in linguistics) creates a challenge. English LLMs benefit from the fact that word position strongly signals grammatical role — "Dog bites man" vs. "Man bites dog" are completely different. In Sanskrit, the case suffix does all that work, and position is stylistic. So the model cannot rely on positional patterns the way a Transformer's attention mechanism naturally does for English.

The kāraka ambiguity problem — your phalam example

This is genuinely hard. Neuter nouns in Sanskrit have identical nominative (kartā/subject) and accusative (karma/object) forms. So phalam pacati could mean "the fruit cooks" or "he cooks the fruit" depending on context. A human uses world knowledge ("fruits don't typically cook things") to disambiguate. LLMs must learn this too — but the training signal is weaker because the form gives no clue. This is a real obstacle.

Where Sanskrit is actually easier

Here's the counterintuitive part: Sanskrit's extreme regularity — codified by Pāṇini's Ashtādhyāyī with ~4,000 sūtras — means the morphology is almost perfectly rule-governed. There are no "irregular verbs" in the chaotic way English or French have them. In principle, a model that truly learns the grammar can generalize better. Researchers have in fact proposed a Pāṇini Sūtra-based AI model that draws inspiration from the Ashtādhyāyī precisely because current LLMs lack deep understanding of Sanskrit's structure, leading to grammatically incorrect and logically flawed outputs.

The data scarcity problem

The bigger practical obstacle is corpus size. India has millions of manuscripts spanning billions of pages, but most are in manuscript form or scanned PDFs that are difficult to process — even copying text from these PDFs often results in corrupted characters, a problem that significantly hinders development. English LLMs train on trillions of tokens; Sanskrit digital text is orders of magnitude smaller.

What's happening in India

A 118-year-old Sanskrit institution has partnered with IIT Madras to build India's first native Sanskrit LLM. Separately, under the IndiaAI Mission, the Government of India has selected Sarvam AI to build India's sovereign LLM, collaborating with AI4Bharat at IIT Madras — designed to be fluent in Indian languages and capable of population-scale deployment.

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Question 2: When does a Vector Database become necessary?

This is a crisp architectural question. Let me explain the spectrum:

What a plain LLM does

A standard LLM (like this conversation) holds all its "knowledge" baked into its weights during training. When you ask it something, it retrieves that knowledge from parametric memory — no external lookup. This is exactly what's happening right now in our conversation. No vector database is needed.

When a Vector Database enters

A vector DB becomes necessary when you have one or more of these conditions:

1. Your knowledge is too large to fit in the context window. Suppose you have 10,000 pages of Sanskrit manuscripts and a user asks a question. You can't paste all 10,000 pages into the prompt. Instead, you convert each passage into a vector (a numerical embedding), store them in the vector DB, and at query time you retrieve only the most semantically similar passages — then feed those into the LLM's context. This is called RAG (Retrieval-Augmented Generation). Research on Sanskrit scripture-based AI has shown that RAG systems are necessary to provide specific and faithful answers from ancient Sanskrit texts, significantly outperforming standard LLMs in producing factual, comprehensive responses with fewer hallucinations.
2. Your knowledge changes frequently. You cannot retrain an LLM every time new data arrives. A vector DB lets you add, update, or delete knowledge dynamically without retraining.
3. You need precise, attributable retrieval. If a user asks "What does verse 3.16 of the Gita say?", an LLM may hallucinate. A vector DB retrieves the exact passage, which the LLM then reasons over.
4. You need to search a private corpus the LLM was never trained on — your company's documents, a personal journal, a proprietary legal database, etc.

The simple summary:

Scenario	Vector DB needed?
General Q&A from training knowledge	No
Chatting, reasoning, coding help	No
Q&A over a large private document corpus	Yes
Keeping knowledge up-to-date dynamically	Yes
Precise citation from specific texts	Yes

In the Sanskrit LLM context specifically, a vector database becomes almost essential for scripture Q&A — because the corpus of Vedic, Purāṇic and philosophical texts is vast, and you want the model to cite specific passages rather than confabulate them from parametric memory.

Me:

So, sentence-transformers does the work of the vector db, for example, in my blog_RAG that I created with your help earlier? Another question is can the knowledge base from which the vector db was created, be created reversely from the vector db? Is it a forward only system? Third question is this: can general maths questions be answered by Sanskrit LLM? For example, 'pi kim asti?' Will the answer be 3.1419...?

Claude:

Three great questions. Let me take them in order.

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Question 1: Does sentence-transformers do the work of the vector DB?

Not quite — they do different jobs in the pipeline. It's worth separating the two roles clearly:

• sentence-transformers is the embedding model — it converts your text chunks into vectors (numerical representations). It answers: "What does this passage mean, mathematically?"
• A vector database (like FAISS, Chroma, Pinecone) is the storage and search engine — it stores those vectors and, at query time, finds the nearest ones. It answers: "Which stored vectors are closest to this query vector?"

In your blog_RAG, you likely used something like FAISS or an in-memory structure alongside sentence-transformers. The sentence-transformer creates the vectors; FAISS does the similarity search. Together they replace what a dedicated vector DB service like Pinecone would do in a production system. So sentence-transformers alone is only half the picture — it's the encoder, not the store.

Think of it this way: sentence-transformers is the librarian who reads every book and writes a summary card. The vector DB is the filing cabinet that lets you pull the most relevant cards quickly.

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Question 2: Can you reconstruct the knowledge base from the vector DB? Is it forward-only?

It is essentially forward-only, and the reconstruction is impossible in any meaningful sense.

Here's why. When sentence-transformers encodes a passage — say, a paragraph from your blog — it compresses potentially hundreds of words into a vector of, say, 384 floating point numbers. This is a lossy, non-invertible transformation. The semantic "meaning" is approximately preserved, but the original words are destroyed. It's analogous to a hash function — you can go from text → hash easily, but not hash → text.

There are a couple of nuances though:

• The original text chunks are usually stored separately alongside the vectors (as metadata). So in practice, production systems keep the raw text and the vectors. The vector DB (like Chroma or Pinecone) stores both. You retrieve the vector, and it hands you back the original passage. So the knowledge base is recoverable — but only because you stored it explicitly, not because you derived it from the vector.
• Approximate inversion research exists — there are adversarial ML papers showing that with enough effort you can partially reconstruct text from embeddings, which is actually a privacy concern in sensitive applications. But this is far from reliable or practical.

So the design principle is: always preserve your original corpus. The vector DB is an index, not a replacement for the source.

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Question 3: Can a Sanskrit LLM answer "pi kim asti?"

This is a delightful question, and the answer is: it depends entirely on what the model was trained on, not on the language itself.

"Pi kim asti?" is perfectly valid Sanskrit — kim (what is), pi (the Greek letter or mathematical constant, borrowed). If the training corpus included mathematical texts or modern Sanskrit writing that discusses π, the model could absolutely answer "tri-dasha-saptati..." or give the decimal expansion.

But here's the deeper point: mathematical reasoning is language-agnostic at the level of the underlying model weights. The same transformer architecture that does arithmetic in English can do it in Sanskrit — if trained on sufficient Sanskrit mathematical text. Sanskrit actually has a strong historical tradition here: the Āryabhaṭīya, Līlāvatī, and Brāhmasphutasiddhānta are full of mathematics expressed in Sanskrit verse. Āryabhata computed π ≈ 3.1416 in the 5th century CE, expressed in Sanskrit.

The practical reality for a current Sanskrit LLM though: the training corpus is small, and mathematical content within it is tiny. So today's Sanskrit LLM would likely fail at this — not because Sanskrit can't express it, but because the model hasn't seen enough Sanskrit math text to learn it. A workaround being explored is cross-lingual transfer — leveraging a multilingual base model that already knows mathematics in English/Hindi, then fine-tuning it on Sanskrit, so the mathematical reasoning ability transfers across languages.

In short: π knows no language. The constraint is data, not grammar.

Me:

Can you please give examples of these two opposing constructs? And what do you mean when you say: In principle, a model that truly learns the grammar can generalize better (with the italics in 'generalize')?

Claude:

Great question — you're asking me to unpack two things: the opposing constructs (regular Sanskrit vs. irregular English), and what "generalize better" really means in the context of ML. Let me do both carefully.

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The Two Opposing Constructs

English: Irregular, Unpredictable Morphology

In English, a model must essentially memorize each irregular form, because no rule predicts it:

Base	Past Tense	Why?
walk	walked	regular
run	ran	no rule — must memorize
go	went	completely suppletive — different root!
fly	flew	vowel change, unpredictable
cut	cut	unchanged — no signal at all

A child (or LLM) learning English cannot say: "I know the verb 'go', therefore I know its past tense." It must have seen went explicitly in training data. If it hasn't, it will say "goed" — which is actually a famous error children make, showing they over-apply the regular rule.

For an LLM, this means: the model needs massive data exposure to encounter all these irregular forms enough times to learn them reliably.

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Sanskrit: Rule-Governed, Predictable Morphology

Sanskrit verb conjugation follows Pāṇini's system with very high regularity. Take the root √pac (to cook):

Person	Singular	Dual	Plural
3rd	pacati	pacataḥ	pacanti
2nd	pacasi	pacathaḥ	pacatha
1st	pacāmi	pacāvaḥ	pacāmaḥ

Now take a completely different root, √gam (to go), same class:

Person	Singular
3rd	gacchati
2nd	gacchasi
1st	gacchāmi

The endings are identical. The pattern is the same. A model that has truly internalized the suffix pattern -ti / -si / -āmi can correctly conjugate a verb root it has never seen before — simply by applying the rule.

This is the opposing construct: Sanskrit rewards rule-learning; English rewards memorization.

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What "Generalize Better" Means

In machine learning, generalization means performing correctly on inputs you have never seen during training.

A model that has memorized 10,000 English irregular verbs will fail on the 10,001st if it wasn't in training data. It has learned a lookup table, not a rule.

A model that has truly learned Sanskrit's suffix system can encounter the rare root √vraśc (to cut) and still correctly produce vraścati in the third person singular — because it applies the internalized rule rather than recalling a memorized form.

The italics on generalize were deliberate: it signals that this is the core virtue we want in any intelligent system — the ability to handle novelty correctly. Sanskrit's structure, in principle, makes this easier to achieve because:

1. The signal is consistent — the same rule applies thousands of times across the corpus
2. The model doesn't need to see every word form — it can derive unseen forms from seen rules
3. Pāṇini essentially wrote the grammar as an explicit finite rule system — which maps naturally onto what neural networks are trying to learn implicitly

The caveat — and why I said "in principle" — is that current LLMs learn statistically from data, not by being explicitly handed Pāṇini's rules. Whether they actually learn the deep rule structure, versus just approximating it from pattern frequency, is still an open research question. The researchers proposing the Pāṇini Sūtra-based model are essentially arguing: why learn the rules implicitly from data when Pāṇini already wrote them down? Feed the rules in explicitly as an inductive bias.

That is a genuinely interesting architectural idea.

 

Me:

What about coining new words, or neologism, as we call in English. In Sanskrit, we are told coining new words also follows a predictable pattern. For example, the word 'computer' was not there in Sanskrit earlier(to be fair, it wasn't there in English either), but has been coined as 'sanganakah' now. Is there a pattern which LLMs can follow?

Claude:

What a wonderful extension of the previous thought. Yes — and this is actually where Sanskrit becomes philosophically superior to English for LLM-based neologism, not just comparable.

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How English Coins New Words

English neologism is essentially borrowing + accident:

• Computer — from Latin computare (to reckon). Borrowed, not derived.
• Telephone — Greek tele (far) + phone (sound). Borrowed roots stitched together.
• Selfie, googling, tweet — purely cultural/arbitrary coinages. No rule predicts them.
• Bluetooth — named after a Viking king. Completely opaque semantically.

An LLM cannot generate a new English word for a new concept by rule. It can only imitate the style of existing coinages, which is a very weak generative capacity. There is no English equivalent of Pāṇini that tells you: "given concept X, here is the word."

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How Sanskrit Coins New Words — The Kṛt and Taddhita System

Sanskrit has a systematic derivational morphology — meaning the process of building new words from roots is itself rule-governed. Pāṇini dedicates enormous sections of the Ashtādhyāyī to this. The key mechanisms are:

1. Verbal roots (dhātu) + suffixes → nouns, adjectives, agent words

The root encodes the action, the suffix encodes the role or quality:

Root	Meaning	Suffix	Derived Word	Meaning
√gam	to go	-aka	gāmaka	one who goes
√pac	to cook	-aka	pācaka	cook, one who cooks
√saṅgan	to compute/reckon together	-aka	saṅganaka	one who computes → computer

Notice saṅganaka (your saṅgaṇakaḥ): it comes from sam (together) + gana (to count/reckon) + -aka (agent suffix). The word is not arbitrary — it is semantically transparent. Anyone who knows Sanskrit morphology can decode it instantly without being told what it means.

2. Compound formation (Samāsa) — near-unlimited productivity

Sanskrit's compounding rules (Tatpuruṣa, Bahuvrīhi, Dvandva, etc.) allow arbitrarily complex concepts to be expressed as single words:

• Doordarshan (television): dūra (far) + darśan (seeing) — "that which enables seeing far"
• Vimāna (aircraft): vi (through/across) + māna (that which measures/moves) — "that which traverses"
• Antarjāla (internet): antar (within/between) + jāla (net/web) — "the inner web"

Each of these is a rule-following derivation, not an arbitrary label.

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What This Means for an LLM

Here is where it gets really interesting architecturally. If an LLM truly learns Sanskrit's derivational system, it gains something English-trained LLMs cannot have: a generative word-formation grammar.

Given a new concept — say, large language model — a Sanskrit-trained LLM could, in principle:

1. Identify the semantic primitives: large + language + model/pattern
2. Find the appropriate roots: bṛhat (large) + bhāṣā (language) + nidarśana (model/exemplar)
3. Apply compounding rules: bṛhadbhāṣānidarśanam
4. Verify the derivation is Pāṇini-compliant

This is not speculation — the Samskrita Bharati organisation and various Sanskrit computational linguistics groups have been doing exactly this manually for decades, coining words like:

• Saṅgaṇaka — computer
• Antarjāla — internet
• Dūradarśinī — mobile phone (far-seeing device)
• Cālakavartiḥ — battery (moving wick/power)

The LLM advantage: if the model has internalized Pāṇini's kṛt and taddhita suffix rules, it can automate this coinage process. You describe a concept in Sanskrit, the model identifies the appropriate roots and suffixes, and produces a new word that is:

• Semantically transparent (decodable from its parts)
• Grammatically valid (Pāṇini-compliant)
• Culturally rooted (uses native Sanskrit roots, not borrowed ones)

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The Deeper Point

English neologism is opaque and arbitrary — laser is an acronym, quark was taken from James Joyce, meme was coined by Dawkins from Greek. Future speakers cannot decode these from first principles.

Sanskrit neologism is transparent and rule-governed — a speaker a thousand years from now could encounter saṅgaṇaka and derive its meaning from morphology alone, even without a dictionary.

This is precisely why some computational linguists argue Sanskrit is not merely a historical curiosity but potentially a superior substrate for knowledge representation in AI systems — the structure of the language itself encodes meaning compositionally, which is exactly what vector embeddings are trying to approximate statistically. Sanskrit does it symbolically and explicitly, at the level of the word itself.

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