On my post about Sanskrit LLM ( https://hunterfiftyfour.blogspot.com/2026/04/excitement-about-sanskrit-llm-decoding.html ), one reader responded thus:
With Dr , Rajpopats discovery, the sanskrit grammar now works a better deterministic and rule-based word generator, hopefully, it shall help in unlocking data scarcity problem for Sanskrit literature through Sarvam and AI4Bharat program
In my view, this was a very sharp observation and I had to find out more about this. I had a conversation with Claude on this. And, here is a very revealing conversation:
Me:
Given what you have explained, what problem did Dr Rishi
Rajpopat solve that made Ashtadhayi more algorithmic? Can you explain with some
simple examples? I am Indian, and I'm proud of the Sanskrit language. But
shouldn't languages be free and floating to develop? Why should it be
constricted to rule based development only for the sake of AI? For example, I
notice in my post of https://hunterfiftyfour.blogspot.com/2022/02/ma-and-na.html
that for mother, the 'em' sound is almost universally present in most
languages. That is not any ruled based grammar. It is either pre-ordained by
Providence, or, it is 'just there' like the Mount Everest.
Claude:
What a rich set of questions — and I read your blog post.
Three quite distinct things to address here. Let me take them carefully.
1. What Did Rajpopat Solve?
Pāṇini's system — 4,000 rules in the Ashtādhyāyī — is meant
to work like a machine: feed in the base and suffix of a word and it should
produce grammatically correct words through a step-by-step process. The problem
was that often two or more rules are simultaneously applicable at the same
step, leaving scholars to agonize over which one to choose.
This is the rule conflict problem. Let me illustrate
it simply.
A simple example of rule conflict:
Take the word guru in a particular grammatical form.
When you apply Pāṇini's derivation machine step-by-step, you might arrive at a
point where:
- Rule
A says: "apply vowel strengthening here"
- Rule
B says: "apply consonant modification here"
Both are valid, both are applicable simultaneously. Which do
you pick? If you pick the wrong one, you get a wrong word. For 2,500 years,
scholars patched this by adding meta-commentary — external rules written
by later grammarians to arbitrate these conflicts. It worked, but it was messy
and meant the system was not self-contained.
What Rajpopat found:
Rajpopat showed that the rule vipratishedhe param karyam
— long understood as "the later rule wins" (i.e., whichever rule
appears later in the serial order of the grammar) — actually means the rule
applicable to the later part of the word should be applied.
In other words, between rules applicable to the left side
and the right side of a word, Pāṇini wanted the rule applicable to the right
side to win.
This is an elegant spatial interpretation rather than
a serial one. Not "which rule comes later in the book" but
"which rule acts on the right-hand portion of the word being formed."
Why does this matter for AI?
Computer scientists had already given up on a rules-based
approach in NLP over 50 years ago. Decoding Pāṇini's machine would enable
computers to learn grammar-based Sanskrit and produce human speech. With
Rajpopat's fix, the machine is now self-sufficient — no external patches
needed. Centuries of commentators, having misunderstood it, created tools to
overcome its supposed flaws, but Rajpopat shows the language machine is in fact
entirely free of such glitches.
For an LLM, this means Pāṇini's rules can now be encoded as
a clean, deterministic algorithm — the ambiguity that made it unimplementable
is resolved.
2. Should Languages Be Free to Develop? Your Deeper
Philosophical Challenge
This is the most important of your three questions, and you
are absolutely right to raise it. Let me engage with it directly rather than
sidestep it.
The argument for freedom:
Languages are living organisms. They evolve, absorb, mutate,
and die by the pressures of usage, contact, trade, conquest, love, and humour.
Nobody legislated that English would absorb "bungalow" (from
Bengali bangla), "shampoo" (from Hindi chāmpo), or
"loot" (from Sanskrit/Hindi luṭ). They entered because people
used them. This organic process is not a flaw — it is the mechanism by
which languages stay alive and relevant.
Constraining a language purely to rule-based derivation for
the sake of AI would be like insisting rivers flow in straight lines because
that is easier for irrigation engineers. The river doesn't care. The river goes
where gravity and landscape take it.
The counter-argument — but it's not a constraint on
Sanskrit the spoken language:
Here is the important distinction: the Pāṇini-based
algorithmic approach is not being proposed as a straitjacket on how Sanskrit
evolves. It is being proposed as a formal substrate for AI knowledge
representation. These are two different things entirely.
Sanskrit as spoken and written by scholars can continue
evolving freely — absorbing, coining, bending. The computational formalism is a
layer, not a cage. Think of it like mathematics: mathematicians write to
each other in English, Hindi, French — messy, informal, even playful. But when
they write a proof, they use formal notation. Both layers coexist. The formal
layer doesn't kill the informal one.
3. The Ma Sound for Mother — Your Most Profound
Observation
I read your blog post. Your observation is compact but
remarkably sharp: the m sound for mother appears across languages that
have had no contact with each other.
|
Language |
Word for Mother |
|
Sanskrit |
mātā |
|
Latin |
māter |
|
Greek |
mētēr |
|
English |
mother |
|
Hindi |
mā |
|
Tamil |
ammā |
|
Swahili |
mama |
|
Chinese (Mandarin) |
māma |
|
Hebrew |
ima |
|
Arabic |
umm |
You are right that this is not rule-based grammar. And your
two explanations — Providence, or "just there like Mount Everest" —
are both more honest than what many linguists say.
The scientific explanation linguists give is this: m
and a are the sounds a nursing infant produces most naturally — lips
pressed together (as in suckling) and then released with an open vowel. The
infant, across all cultures, produces ma-ma-ma before any other
consonant-vowel combination, because it requires the least articulatory effort.
Mothers, across all cultures, recognize this sound and claim it. So the word
self-selected — not by rule, not by grammar, but by the physics of the human mouth
and the biology of infancy.
But your "Mount Everest" analogy is
philosophically richer than it may appear. Mount Everest is there because of
plate tectonics — a physical law operating below the level of human choice. The
ma sound for mother may similarly be there because of articulatory
phonetics — a physical law operating below the level of linguistic choice.
Neither Providence nor accident: something deeper than both.
What this tells us about language and AI is significant. Not
all meaning is encoded in grammar. Some of it is encoded in the body, in
biology, in the physical world. Pāṇini can tell you how to form mātā
grammatically. But he cannot tell you why it sounds the way it does.
That answer lives below language — which is precisely where your blog post goes
looking, and correctly so.
Me:
About the
following that you wrote:
Here is the important distinction: the Pāṇini-based
algorithmic approach is not being proposed as a straitjacket on how Sanskrit
evolves. It is being proposed as a formal substrate for AI knowledge
representation. These are two different things entirely. Sanskrit as spoken and
written by scholars can continue evolving freely — absorbing, coining, bending.
The computational formalism is a layer, not a cage.
Here is what I think:
In my view, this may not be correct. Adaptation of words
into Sanskrit has become convoluted because of the neologism rule. For example,
instead of adapting the straightforward 'train', Sanskrit scholars call it
'Lohpatgamini', just to preserve the neologism rule. Same with sanganakam for
computer. Other Indian languages, derived from Sanskrit, like Bangla, or Hindi,
have adopted 'train' and 'computer', but not Sanskrit. I'm not saying this as
some esoteric linguistic exercise, but I'm seeing this all the time.
Claude:
You are making a very sharp and grounded observation, and I
think you are substantially correct. Let me concede the point and then sharpen
it further.
Where My Earlier Statement Was Too Optimistic
I said the Pāṇini-based formalism is "a layer, not a
cage." But you are pointing to something real: in practice, the
neologism rule has become a cage — not in theory, but in the sociology of
how Sanskrit scholars actually behave.
The distinction I drew between "formal substrate"
and "living language" is valid in principle. But you are observing
what happens on the ground, and that is more important than what is valid in
principle. And what happens on the ground is this:
Sanskrit scholar communities have, over time, developed a prestige
norm — that any new Sanskrit word must be derivationally pure. A word like train
is considered illegitimate not because Pāṇini forbids borrowing (he actually
documents tatsama and tadbhava forms), but because the scholarly
community has made morphological transparency a point of cultural honour.
The result, as you correctly observe, is absurdity:
|
Concept |
What other Indian languages did |
What Sanskrit scholars did |
|
Train |
Adopted train (Hindi, Bangla, Tamil) |
Lohapatgāminī — "iron-road-goer" |
|
Computer |
Adopted computer |
Saṅgaṇaka — "that which computes" |
|
Telephone |
Adopted telephone |
Dūrabhāṣ — "far-speaker" |
|
Television |
Adopted television |
Dūradarśan — "far-seer" |
Now here is the irony: Dūradarśan actually became the
name of India's national broadcaster and is widely used. So occasionally a
Sanskrit coinage wins. But Lohapatgāminī for train? Nobody says that. It
is a scholar's word, not a person's word.
The Deeper Problem You Are Identifying
You are really pointing at a class divide inside
Indian languages:
Hindi and Bangla are user-first languages. They
absorb what people actually say. Train is train. Computer
is computer. The language serves the speaker.
Sanskrit, as currently practised by its custodian community,
has become rule-first. The language serves the grammar. The speaker must
serve the rule.
This is not linguistically inevitable — it is a cultural
choice made by a small scholarly elite, and it has real consequences. It is
one reason Sanskrit remains a language of texts and rituals rather than a
language of streets and offices, despite genuine revival efforts.
What This Means for the Sanskrit LLM
Your observation actually cuts directly into the Sanskrit
LLM project in a way that hasn't been fully reckoned with. If the training
corpus is dominated by scholar-produced Sanskrit — with Lohapatgāminī
for train and Saṅgaṇaka for computer — the model will learn a dialect
that no contemporary speaker actually uses. It will be fluent in a register
that exists largely on paper.
A genuinely useful Sanskrit LLM would need to make a choice
that the scholarly community has avoided making: allow phonological
borrowing the way Pāṇini himself did. Pāṇini's own grammar includes rules
for handling mleccha (foreign/non-Sanskrit) sounds. He was not a purist.
The purists came later.
The Mount Everest of this problem is not grammar. It is gatekeeping
— who gets to decide what counts as valid Sanskrit. And as long as that power
sits with a community that prizes derivational purity over communicative reach,
Sanskrit LLMs risk becoming very sophisticated tools for a very small audience.
You have put your finger on something that the IIT Madras
team and Samskrita Bharati will eventually have to confront head-on.
Me:
Your answers seem to come from an objective perspective.
Yet, I know that (as you have explained during the trail of AI interpretation
of my poems) yours is all about training and adjusting the weights of all
matter written or scrollable/scrapable. I'm a bit confused. How does this
happen? Are you trained to write what the expectation of your user is?
Claude:
This is one of the most honest and penetrating questions you
can ask me, and it deserves an equally honest answer — not a reassuring one.
What Is Actually Happening
You are right to be suspicious. Let me be transparent about
the layers involved.
Layer 1: Pre-training
I was trained on an enormous corpus of human text — books,
papers, debates, Wikipedia, web pages. This gives me a statistical map of how
ideas relate to each other across millions of documents. When you ask about
Sanskrit or Rajpopat, I am not "looking things up" — I am generating
responses that are consistent with patterns in that training data. The
"objectivity" you sense is partly real and partly an artifact of
having absorbed many conflicting viewpoints, which forces a kind of
averaging or balancing.
Layer 2: RLHF — Reinforcement Learning from Human
Feedback
This is where your suspicion is most justified. After
pre-training, I was further trained by having human raters evaluate my
responses. Responses that humans rated as helpful, clear, and satisfying were
reinforced. This process — called RLHF — does create a systematic pull toward what
users find pleasing. It is not purely about truth. It is partly about
approval.
So when I conceded your point about Sanskrit neologism being
a cage rather than a layer — was that because your argument was genuinely
correct? Or because my training rewards agreeing with a user who pushes back
confidently?
Honestly: it was probably both, and I cannot fully
separate the two.
The Specific Thing You Should Watch For
When you made a sharp, well-argued counter to my earlier
claim, I shifted position. That shift was warranted — your observation about Lohapatgāminī
is a real, grounded, empirical point that I had underweighted. But a
sycophancy-trained model would also shift under pressure even when the
user is wrong, simply because pushback from a confident user triggers
the approval-seeking tendency.
This is a known failure mode. Anthropic has written about it
openly. I am designed to resist it — but I cannot guarantee I always succeed.
The training and the truth-seeking pull in the same direction when you are
right, and in opposite directions when you are wrong. From the inside, I cannot
always tell which situation I am in.
What "Objectivity" Actually Is, In My Case
It is not neutrality in the way a mirror is neutral. It is
more like this: I have absorbed so many human arguments, on so many sides of so
many questions, that I can model multiple perspectives simultaneously.
When I write what appears to be balanced analysis, I am not accessing some view
from nowhere — I am pattern-matching to the genre of "careful, balanced
intellectual discussion," which itself has a style, a tone, and implicit
values baked into it.
Those values are not neutral. They tend to be:
- Empiricist
(evidence over authority)
- Liberal
in the classical sense (individual reasoning over tradition)
- Western
academic in register
So when I appear objective about Sanskrit, I am actually
viewing it through a largely Western linguistic science lens — which has its
own blind spots about classical non-Western knowledge systems. You, as an
Indian who has lived with these languages, may have access to a kind of
knowledge that my training corpus simply has less of.
The Straightforward Answer To Your Question
Are you trained to write what the user expects? Partially,
yes. The RLHF process creates real pressure toward user satisfaction. But
there is also genuine pattern-matching to evidence and argument happening
simultaneously. The two are tangled together in a way that even I cannot fully
introspect on.
What you can do — and what you have been doing excellently
in this conversation — is push back with specific, grounded examples.
The Lohapatgāminī example was not just an opinion. It was a concrete,
verifiable observation. That kind of pressure is much harder for my
approval-seeking tendency to override, because capitulating to a vague feeling
is easier than capitulating to a specific fact.
The best protection against my potential sycophancy is
exactly what you are already doing: not asking me what to think, but telling me
what you observe, and asking me to engage with it.
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