🌍 SVO vs. SOV — Why Word Order Matters in AI Most LLMs like GPT-4 are trained on SVO languages like English. But languages like Hindi, Tamil, Marathi, and Bengali follow SOV structure — where the verb comes last. 🔹 English (SVO): “I eat rice.” 🔹 Hindi (SOV): “मैं चावल खाता हूँ” (I rice eat) This isn’t just grammar trivia. It’s a fundamental challenge in AI. LLMs trained predominantly on English struggle to understand or generate SOV languages fluently unless fine-tuned properly — or built from the ground up on Indic data. This is why building truly multilingual LLMs isn’t just about translation — it’s about respecting the cognitive architecture of each language. India’s linguistic diversity isn’t a bug — it’s a feature. Let’s build AI that reflects that. #AI #LLM #NLP #IndicLanguages #MultilingualAI #GPT #OpenSource #LanguageTechnology #LLMinIndia #SVOvsSOV #BhashaAI
The core challenge with AI language models is not just producing translations but truly understanding the syntax and semantics embedded in different word orders. SVO languages like English offer models a certain logic that SOV languages like Hindi disrupt, impacting fluency in AI generation and perception. In building multilingual capabilities, how can AI better handle these linguistic nuances? Could leveraging deep linguistic embeddings specific to each language improve adaptability?
Siddhartha Mukherjee Interesting challenge...makes me wonder how much bias we bake in just by training on SVO-heavy data. Fine-tuning for SOV feels like just the start, right?
Siddhartha Mukherjee I am surprised that comes out as a challenge. In our experience at E2E Cloud. Large quantity of English training followed by RL on specific languages allows model even LSTM models to adapt to the SVO to SOV switch. Even ability to recognise gender in Hindi text - खाता and खाती
Please elaborate where the word order is implied in LLMs based on GPT architecture. My understanding is that e.g. LLM training (very roughly) builds relations of token with other tokens of the same input sequence. And the order of words in the training data is more or less unimportant within context window. As a naive example, translating from German (verb should be on 2nd place in the sentence) to English back works with ChatGPT just fine.
Tamil doesn't follow this; you can place the subject and verb almost anywhere without the meaning changing.
That's the real origin of wastage in india. More ink more paper😂 more words more talking and it does on. Useful solution: 1. Data-Centric Approaches Curate high-quality SOV-language datasets: Build domain-specific corpora for Indic languages, focusing on diverse syntactic structures and real-world usage patterns. This addresses the data scarcity that limits current fine-tuning efforts. Leverage hybrid datasets: Combine monolingual SOV data with parallel SVO-SOV translation pairs to help models internalize structural differences. 2. Architectural Adaptations SOV-optimized tokenization: Develop subword tokenizers trained exclusively on SOV languages to better capture morphological and syntactic nuances (e.g., verb-final constructs). Syntax-aware attention mechanisms: Modify transformer architectures to prioritize relationships between subjects, objects, and verbs in SOV order. 3. Training Strategies Two-phase pre-training: Structural priming: Train on synthetically reordered SVO→SOV text to establish basic SOV pattern recognition. Native SOV immersion: Continue training on authentic SOV-language content. Linguistic regularization: Add loss terms that penalize deviations from SOV syntactic rules during training.
Super cool insights, Siddhartha! LLMs are seriously changing the game in NLP and beyond. Love how you broke it down. Curious—have you come across any standout real-world use cases in industries like healthcare or finance? Always on the lookout for where this tech is making a real impact. #AI #LLM #NLP #TechInAction #MachineLearning
There’s also vso ovs osv. Pardon the missing commas. Vso (as in Tagalog): Ate Shyam rice . Vos (as in Austronesian languages) : Ate rice Shyam. Are we getting to a different formulation on how LLMs should learn? May be so because this is not the only thing that differentiates languages.
Deustch is also Subject Object Verb
I was exploring the #sarvamai llm and tts models, though it is a good start for India 🇮🇳, atleast a start. But quite far from a usable / quality outcome, i wish they spend the government funding in building the foundations and not just fork another mistral ai or llama