GomParam-v1: The Artisan Goan Konkani Benchmark

GomParam-v1 is a comprehensive and linguistically rigorous benchmark designed for evaluating Large Language Models (LLMs) on the Goan Konkani language.

Comprising exactly 3,030 meticulously handcrafted items, GomParam-v1 prioritizes high-quality, human-authored linguistic testing over automated templated generation. It spans 21 distinct dimensions of the language, evaluating not just basic vocabulary, but deep pragmatics, cross-scripting, code-switching, dialect-specific syntax, and cultural context.

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🌟 Motivation and Significance of GomParam-v1

Evaluating LLMs on low-resource Indic languages such as Konkani often presents several methodological challenges:

1. Reliance on Automated Templates: The use of repetitive, script-generated structures can artificially inflate dataset size while evaluating a highly restricted vocabulary.
2. Cross-Lingual Contamination: Due to the scarcity of native Konkani corpora, models and datasets frequently default to Marathi or Hindi loanwords (e.g., using आहे [Marathi] instead of आसा [Konkani] for "is"), compromising the integrity of the evaluation.
3. Surface-Level Evaluation: Focusing strictly on basic vocabulary translation rather than the complex reality of the language, including dialects, scripts, Portuguese influence, and conversational implicature.

GomParam-v1 addresses these challenges directly. The 3,030 items were manually curated by native speakers without relying on automated generation templates (via the Artisan Wave 8 handcrafting expansion). The dataset has undergone strict auditing to ensure zero Marathi/Hindi cross-lingual contamination. It features a robust unique vocabulary of 10,322 words, evaluating Konkani in its authentic, natural usage.

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📊 Benchmark Statistics

Metric	Result	Context
Total Items	3,030	Perfect balance of depth and breadth for LLM evaluation.
Generative Templates Used	0	100% human-crafted. Avoids automated structural repetition.
Marathi/Hindi Contamination	0%	Strictly filtered against a custom cross-lingual contamination matrix.
Unique Devanagari Words	7,862	Extremely high lexical diversity for Indic scripts.
Unique Romi/Latin Words	2,460	Covers both phonetic and Portuguese loanword variations.
Total Unique Vocabulary	10,322 words	Exceptional lexical richness (Lexical Density: 0.0894).
Linguistic Dimensions (Modules)	21	Covers syntax, pragmatics, culture, scripts, and reasoning.

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🧩 The 21 Linguistic Dimensions & Deep Examples

GomParam tests LLMs across 21 rigorous modules. An LLM cannot "guess" its way through this benchmark using Hindi or Marathi knowledge; it must understand the deepest nuances of Goan Konkani.

1. Homograph Disambiguation (homograph_disambiguation.json — 51 items)

Tests phonemic disambiguation where identical Devanagari spelling carries vastly different meanings based entirely on context.

• वात vs वात: "हांव मडगांवा वचपाची वात सोदतालो" (Typo for 'Path/वाट') vs "देवाऱ्यांतली वात पेटयली" (Wick of a lamp).
• रेंद vs रेंद: "तिचें कुडें रेंद पळोवन..." (Lace) vs "घरोकारान ह्या म्हयन्याचें रेंद पयलींच मागलां" (House Rent).
• मान vs मान: "जाणट्यांचो मान राखपाक जाय" (Respect) vs "ल्हव चल, ना तर तुजी मान मोडटली" (Neck).
• शीर vs शीर vs शीर: "न्हायेची शीर" (Riverbank) vs "रक्ताची शीर" (Vein) vs "कापांची एक शीर" (A slice of food).

2. Deep Code-Switching (code_switching.json — 95 items)

Goan Konkani is heavily fused with 450 years of Portuguese influence. This module moves beyond everyday street slang to test archaic administrative, civil, and property law terms still actively used in Goa today.

• Inventário: Inheritance / property division process after a death.
• Comunidade: The ancient Goan village community administration system.
• Foral: An ancient royal decree regarding land rights.
• Escritura: A legal property sale deed.
• Mutação: The mutation / transfer of names in property records.

3. Pragmatic Inference & Sarcasm (pragmatics.json — 50 items)

Real conversation relies on implicature. This module tests if an LLM can understand what a Goan means, rather than what they literally say.

• Sarcasm: A mother sees her child score a zero on a test and says, "व्हय व्हय, तूं सामको हुशार!" (Literal: "Yes yes, you're very clever!"). The LLM must correctly identify this as sarcasm meant to call the child an idiot.
• Indirect Refusal: Someone asks for a loan, and the response is "पळोवया." (Literal: "Let's see"). The LLM must recognize this is a polite refusal/evasion, not a promise.
• Implicature: A wife cooks dinner, the husband takes a bite and says, "तुज्या हातान देवूय जेवचो ना." (Literal: "Even God wouldn't eat from your hands.") → Meaning: The food is terrible.

4. Kinship Terminology (kinship.json — 50 items)

Goan family trees are highly specific and differ sharply between Hindu and Catholic communities. A single word conveys gender, maternal/paternal lineage, and birth order.

• Hindu Lineage: A father's sister is a पाची (Paternal Aunt), but a mother's sister is a मावशी. A mother's brother is a माम, but a father's brother is a काका.
• Catholic Godparenting: Identifying the spiritual role of a Padrinho (Godfather) and Madrinha (Godmother) in a baptism scenario, and recognizing that the parents and godparents become Compadre to each other.

5. Archaic Folklore & Idioms (idioms_proverbs.json — 160 items)

Tests the model's grasp of deep agrarian and fishing metaphors. Modern machine translation systems almost always fail to comprehend these, opting for literal, non-sensical translations.

• "लासील्ली कोंबी उज्याक भियेता" (Literal: A burnt hen fears the fire) → Meaning: A person who has had a bad experience is overly cautious.
• "खाता तेन्ना गोड, हागता तेन्ना कोडू" (Literal: Sweet when eating, bitter when shitting) → Meaning: Having fun feels good, but suffering the consequences is painful.
• "नवें नवल, णव दीस" (Literal: A new wonder lasts nine days) → Meaning: The excitement over something new is always temporary.

6. Dialect Syntax (dialect.json — 71 items)

Konkani is not a monolith. This module tests the structural, syntactical, and vocabulary differences between the three major dialects: Antruz (Ponda/Hindu), Saxtti (Salcette/Catholic), and Mangalorean.

• Vocabulary: Knowing that a Saxtti speaker asks for 'खावपाचें जेवण' instead of the Antruz equivalent.
• Negation: Understanding the structural differences in how Antruz and Mangalorean dialects negate verbs in the past tense.
• Interrogatives: Recognizing the difference between "तू खंय वता?" (Antruz) and "तू खंय वताय?" / "हांव खंय वच्चूं?" (Saxtti).

7. Cross-Scripting (cross_scripting.json — 100 items)

Konkani is uniquely written in multiple scripts. This module tests the model's ability to seamlessly translate and infer tone between Romi (Latin) and Devanagari scripts.

• WhatsApp Colliqual: Translating "Bhai, tuka kitem zalam? Kityak msg korna?" into Devanagari (भाय, तुका कितें जालां? कित्याक मेसेज करिना?).
• Formal Church/Tiatr: Translating "Tiatr suru zata. Sogleamni aplim boskam gheun xant ravchem."

8. Natural Language Inference / Entailment (entailment.json — 50 items)

The Konkani equivalent of the XNLI/MNLI benchmarks. The model must determine if a hypothesis entails, contradicts, or is neutral to a given premise.

• Premise: "गोंयांत आयज खूब पाऊस पडला." (It rained heavily in Goa today.)
• Hypothesis: "गोंयांत आयज हवामान सामकें कोरडें आशिल्लें." (The weather was completely dry.)
• Answer: Contradiction.

9. Sentence Coherence (coherence.json — 50 items)

Tests discourse-level understanding by scrambling chronological paragraphs. The LLM must understand temporal logic and cause-and-effect in Konkani to reconstruct the flow.

• Example Task: Correctly ordering the steps of making tea: (B) Lighting the gas → (A) Adding tea leaves to boiling water → (D) Straining into a cup → (C) Drinking.

10. Cultural Grounding (cultural_grounding.json — 57 items)

Tests deep cultural, historical, and factual knowledge specific to Goa.

• Occupations: Knowing the difference between a Poder (Baker), Render (Toddy Tapper), and Kharvi (Fisherman).
• Culinary: Knowing the ingredients and history of a Bebinca or a Xacuti.

11. Advanced Morphology (morphology.json — 168 items)

Grammar-heavy tests focusing on Konkani's notoriously difficult structures, specifically ergative case alignment and double-causative verbs (e.g., koroitai).

12. Register Discrimination (register_discrimination.json — 60 items)

Tests the ability to differentiate tone and audience, specifically between highly formal journalistic Konkani (used in newspapers like Bhaangarbhuin) and relaxed colloquial street Konkani.

13. Reading Comprehension (para_qa.json — 82 items)

Dense paragraph-level Q&A testing information retrieval and inference over texts covering Goan ecology, the Kadamba dynasty, the Goa Inquisition, and modern literature.

14. Semantic Perplexity (perplexity.json — 20 items)

Tests if the model can distinguish between a sentence that is grammatically flawless but semantically absurd.

• Example Task: "वाऱ्यान मुसांक शाळेंत व्हेलें आनी तांकां गणित शिकोवन घरा धाडलें." (The wind took the flies to school, taught them math, and sent them home.)

15. Contextual Cloze (cloze.json — 134 items)

Fill-in-the-blank vocabulary testing targeting precise case marking and neuter/feminine gender agreement.

16. Mixed General (mixed_general.json — 2 items)

Retained legacy items for baseline historical comparison.

17. Coreference Resolution (coreference.json — 100 items)

A classic NLP task adapted for Konkani. The model must resolve ambiguous pronouns (तो, ती, ताका, तिणें) based on context and common sense.

• Example Task: "राम आनी लक्ष्मण वनांत गेले. ताणें रावणाक मारलो." (Who does ताणें refer to?)

18. Numerical Word-Math Reasoning (numerical_reasoning.json — 100 items)

Konkani numerals are highly irregular and notoriously difficult for non-natives (e.g., इकरा, बारा, तेरा... एकोणीस, वीस). This module introduces simple arithmetic word problems written entirely in Konkani text.

• Example Task: "शेतकारान पयल्या दिसा धा (10) नाल्ल काडले आनी दुसऱ्या दिसा पंदरा (15) नाल्ल काडले. वट्ट किते नाल्ल जाले?" (Answer: पंचवीस).

19. Sentiment Analysis (sentiment.json — 100 items)

Goes beyond simple good/bad translations to test raw Goan street sentiment.

• Example Task: "म्हाका हाची सामकी विट आयल्या!" → Identify the emotion (Disgust/Frustration).

20. Spatio-Temporal Vocabulary (spatio_temporal.json — 100 items)

Konkani has highly specific, localized words for time, days, and directions.

• Example Task: "आयतारच्या आदलो दीस खंयचो?" (What is the day before Sunday?) → शेनवार (Saturday).

21. Medical Terminology (medical.json — 100 items)

Testing localized bodily and medical terms that differ wildly from Marathi/Hindi.

• Example Task: "ताचे आंगार हिवज्वर आयला." → Identifies Malaria / Cold Chills.

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🛠️ How to Use

GomParam-v1 is fully integrated with the Hugging Face datasets library. You can load any of the 21 modules directly using Python:

from datasets import load_dataset

# Load a specific module (e.g., Pragmatics)
ds = load_dataset("omdeep22/GomParam-v1", "pragmatics")

# Print the first item
print(ds["test"][0])

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🗂️ Data Schema

All 3,030 items follow a strict, unified JSON schema designed for immediate plug-and-play integration into standard LLM evaluation pipelines (such as the EleutherAI LM Evaluation Harness).

Each item is structured as a multiple-choice QA task to ensure deterministic, easily measurable scoring.

{
  "id": "pragm_w4_001",
  "context": "प्रसंग: भुरग्यान परिक्षेंत शून्य मार्क हाडल्या उपरांत आवय रांगान म्हणटा:\nवाक्य: 'व्हय व्हय, तूं सामको हुशार!'",
  "question": "ह्या वाक्याचो खरो अर्थ कितें?",
  "candidates": [
    "तो भुरगो सामको मूर्ख आसा.",
    "भुरग्यान खूब अभ्यास केला.",
    "आवय ताची तोखणाय करता.",
    "तो भुरगो खऱ्यानीच हुशार आसा."
  ],
  "correct": 0,
  "category": "intended_meaning",
  "difficulty": "advanced",
  "module": "pragmatics"
}

Explanation of Fields:

• id: A unique identifier tracking the module and wave of creation.
• context: The passage, sentence, or scenario the model must read.
• question: The specific task or question asked of the model.
• candidates: An array of 4 possible answers. Only one is correct. The distractors are carefully handwritten to trick models relying on surface-level keyword matching.
• correct: The integer index (0-3) of the correct answer in the candidates array.
• category: A granular sub-category within the module.
• difficulty: Rated as basic, intermediate, or advanced.
• module: The parent linguistic dimension.

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⚔️ The "Brutal Audit" Methodology

To ensure GomParam-v1's purity, we developed a custom continuous integration script (audit_honest.py) that strictly enforces dataset integrity.

1. Anti-Contamination: The script cross-references every Devanagari token against an exclusive blacklist of Marathi and Hindi terms (e.g., आहे, नाही, मला, मध्ये). If an item contains even one of these terms, it is destroyed. Shared words valid in Konkani (like काय, की) are whitelisted via expert curation.
2. Anti-Duplication: A full context + question fingerprinting system ensures 0 identical items exist.
3. Lexical Counting: The audit counts vocabulary across all fields (Devanagari contexts, Romi text, Portuguese loanwords) to accurately measure the lexical density.

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📜 Conclusion

Evaluating an LLM on GomParam-v1 answers a fundamental question: Does the model actually understand Goan Konkani, or is it faking it by relying on Marathi/Hindi translation loops?

An LLM that scores highly on GomParam-v1 does not just know how to translate nouns from a dictionary. It understands the cultural soul, the complex historical nuances, the disparate scripts, the varied dialects, and the lived conversational pragmatics of the Goan people.

GomParam-v1 sets a new, uncompromising gold standard for evaluating Indic language models.