Automated AI evaluation metrics fail to properly capture subtle linguistic errors when Indian languages are written in Roman characters, necessitating new evaluation criteria that account for linguistic characteristics.
Imagine you are living in India right now. When sending a message to a friend, instead of typing out the complex characters of Hindi, you type ‘aap kaise hain (how are you?)’ using the familiar English alphabet. Hundreds of millions of Indians communicate in this way in digital environments. But how well does the Artificial Intelligence (AI) we use understand this way of speaking?
Recent AI research has revealed the shocking fact that the evaluation methods we commonly use fail to catch any of the subtle errors that occur when Indian languages are written in Roman characters based on Latin characters. Source: The Invisible Gap: Romanized Indic Generation Fails Where Automatic Metrics Succeed
Why does this matter?
In the digital age, AI acts as our capable assistant. But what if that assistant cannot properly understand the subtle nuances of our language? In India, hundreds of millions of users manipulate digital devices and search for information through ‘Romanized Indian languages’. Source: The Invisible Gap: Romanized Indic Generation Fails Where Automatic Metrics Succeed If AI does not fully understand this method, local users will be unable to receive fair service from AI and will inevitably be alienated from the digital world.
In particular, in terms of business or search engines, ‘Hinglish’ (a mix of Hindi and English) search is very important. AI engines that cannot read this properly cause opportunity costs for local Indian small business owners, ultimately leading to deepening the information gap. Source: Hinglish AI Search Visibility: Why Engines Misread It
Simply put, what is the problem?
Let’s compare the current AI evaluation method to a ‘multiple-choice test’. Current technical metrics like BGE-M3 and BERTScore mechanically score how linguistically similar the text written by AI is to the original. The problem is that these test papers do not consider the specific nature of Indian languages written in Roman characters at all.
The results of the research team testing the latest models, GPT-5.5 and Claude Opus 4.8, were surprising. According to automated machine metrics, the difference in performance between the two AIs was only 0.004 points, showing almost no difference. However, the scores evaluated directly by actual Indian native speakers differed by a whopping 1.43 points. Source: The Invisible Gap: Romanized Indic Generation Fails Where Automatic Metrics Succeed
This is like an AI mimicking the format of an answer sheet while solving a math problem, but writing the actual content poorly, yet a machine giving it a passing grade because ‘similar words were included’. While the AI looks plausible on the surface, it is producing results that are of no help to actual Indian people.
Where is it going wrong?
Currently, many AI models exhibit two major, serious failure patterns when generating Romanized Indian languages. Source: The Invisible Gap: Romanized Indic Generation Fails Where Automatic Metrics Succeed
- Script Spillage: A phenomenon where local characters suddenly pop up in the middle of a response, even though the instruction was to answer in Roman characters. For example, the Gemini model showed this error in about 17% of Telugu responses.
- Instruction Collapse: A phenomenon where the clear instruction to answer in Romanized Indian language is ignored, and the AI just answers in English. In the case of the Qwen model, about 16% of Kannada responses showed this phenomenon.
These errors are elements that impair the actual user experience and can never be discovered through mechanical statistical evaluation methods.
What will happen in the future?
The research team emphasizes that we must move away from simple similarity calculations. For a true AI evaluation, a ‘comprehensive evaluation package’ that binds the following four elements is needed. Source: The Invisible Gap: Romanized Indic Generation Fails Where Automatic Metrics Succeed
- Semantic Similarity: Is the content to be conveyed accurate?
- Script Compliance: Does it adhere to the notation method instructed by the user until the end?
- Failure Capture: Does it mix in strange characters or change the language in the middle?
- Native Speaker Acceptance: Is it comfortable and natural for actual Indian native speakers to read?
For AI to provide equal performance to countless language users around the world, not just in Indian languages, these ‘user-centric evaluation criteria’ must become technical standards. Benchmarks like the recently introduced Indi-RomCoM will be an important first step toward this change. Source: [2606.30790] Indi-RomCoM: Code-Mixed Benchmark for Evaluating…
MindTickleBytes AI Reporter’s View
As technology advances, it is easy for us to be buried in huge numbers like 1 trillion parameters (a unit representing the scale of an AI’s brain) or processing speed per second. However, the true completeness of AI does not depend on numbers, but on how fully it understands ‘my language’, which is used most naturally in daily life. What is more important than the splendor of technology is how deeply we embrace the way our language views the world.
References
- AI always responds only in English
- There is a significant gap between automated evaluation metrics and actual human evaluation
- AI cannot understand Indian languages at all
- Script leakage and English isolation
- Linguistic errors and grammar destruction
- Data shortage and model overload
- Using more data
- Further refining automated metrics only
- Integrated evaluation considering semantic similarity, script standard compliance, and native speaker acceptance