--- language: - kok license: cc-by-4.0 task_categories: - text-classification - question-answering - fill-mask task_ids: - multiple-choice-qa - natural-language-inference - language-modeling pretty_name: GomParam-v1 size_categories: - n<1K tags: - konkani - low-resource - indic-languages - nlp-benchmark - morphology - language-understanding - goa - devanagari dataset_info: - config_name: cloze splits: - name: test num_examples: 25 - config_name: morphology splits: - name: test num_examples: 20 - config_name: para_qa splits: - name: test num_examples: 12 - config_name: jokes_sayings splits: - name: test num_examples: 16 - config_name: dialect splits: - name: test num_examples: 15 - config_name: perplexity splits: - name: test num_examples: 30 --- # GomParam-v1 — First Dedicated Konkani Language Benchmark **GomParam** (named after *Gomantak*, the ancient Sanskrit name for Goa) is the first comprehensive benchmark designed specifically to evaluate large language models on **Konkani** (ISO 639-3: `kok`) — a severely low-resource Indo-Aryan language spoken by approximately 2.5 million speakers, primarily in Goa, India. > 📄 **Companion model:** [Gonyai-TEO2](https://huggingface.co/omdeep22/Gonyai-teo2) — > 251M parameter Konkani LLM pretrained from scratch. > 📦 **Companion corpus:** [Konkani-Books-Corpus-v2](https://huggingface.co/datasets/omdeep22/Konkani-Books-Corpus-v2) — 86M token Konkani dataset. --- ## Motivation Existing Indic language benchmarks (IndicParam, MILU, IndicGenBench) contain minimal or no Konkani coverage, and those that do test **world knowledge about Konkani culture** rather than **Konkani language ability**. GomParam-v1 fills this gap by testing: - Morphological correctness (verb conjugation, agreement) - Syntactic competence (case marking, postpositions, participles) - Reading comprehension in Konkani - Cultural and pragmatic understanding (proverbs, jokes) - Dialect robustness (Goan vs. Mangalorean Konkani) **No world knowledge is required.** Every question is answerable from language understanding alone, making GomParam-v1 a fair test for any model regardless of its encyclopedic pretraining. --- ## Dataset Structure ### Modules | Module | Items | Task | Scoring | |---|---|---|---| | `cloze` | 25 | Fill-in-the-blank (4-choice) | Log-likelihood MCQ | | `morphology` | 20 | Verb conjugation (4-choice) | Log-likelihood MCQ | | `para_qa` | 12 | Paragraph comprehension (4-choice) | Log-likelihood MCQ | | `jokes_sayings` | 16 | Proverb/joke meaning (4-choice) | Log-likelihood MCQ | | `dialect` | 15 | Goan vs Mangalorean sentence pairs | Perplexity consistency | | `perplexity` | 30 | Held-out sentences | Bits-per-token | | **Total** | **118** | | | **Random baseline:** 25.0% for all MCQ tasks (4-choice). ### Cloze Item Format ```json { "id": "cloze_001", "sentence": "तो उद्यां मुंबयीक ___ वता.", "candidates": ["विमानान", "विमाना", "विमानाक", "विमानानी"], "correct": 0, "category": "case_marking", "note": "instrumental case — travel by plane" } ``` ### Morphology Item Format ```json { "id": "morph_001", "context": "हावें काल एक पुस्तक", "candidates": ["वाचलें", "वाचलो", "वाचली", "वाचतां"], "correct": 0, "category": "ergative_past", "note": "1sg ergative + neuter object past" } ``` ### Para QA Item Format ```json { "id": "para_001", "passage": "गोंय हें भारताच्या पश्चिम दर्यादेगेर...", "question": "गोंय भारताक केन्ना मेळ्ळें?", "candidates": ["१९४७ वर्सा", "१९६१ वर्सा", "१९५० वर्सा", "१९७१ वर्सा"], "correct": 1, "category": "factual_extraction" } ``` ### Dialect Item Format ```json { "id": "dialect_004", "goan_dev": "आमी उद्यां येतलो.", "mang_dev": "आमी फाल्यां येतलो.", "gloss": "We will come tomorrow.", "lexical_diff": true } ``` --- ## Usage ```python from datasets import load_dataset # Load individual modules cloze = load_dataset("omdeep22/GomParam-v1", "cloze", split="test") morph = load_dataset("omdeep22/GomParam-v1", "morphology", split="test") para = load_dataset("omdeep22/GomParam-v1", "para_qa", split="test") jokes = load_dataset("omdeep22/GomParam-v1", "jokes_sayings", split="test") dialect = load_dataset("omdeep22/GomParam-v1", "dialect", split="test") ppl_sents = load_dataset("omdeep22/GomParam-v1", "perplexity", split="test") ``` ### Evaluation (log-likelihood MCQ) ```python import torch import numpy as np from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "omdeep22/Gonyai-teo2" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True) model.eval() @torch.no_grad() def score_completion(prompt, completion): full = prompt + " " + completion p_ids = tokenizer.encode(prompt, return_tensors="pt") f_ids = tokenizer.encode(full, return_tensors="pt") if f_ids.shape[1] <= p_ids.shape[1]: return float("-inf") logits = model(f_ids).logits opt_start = p_ids.shape[1] opt_logits = logits[0, opt_start - 1:-1, :] opt_targets = f_ids[0, opt_start:] lp = torch.nn.functional.log_softmax(opt_logits, dim=-1) return lp[range(len(opt_targets)), opt_targets].mean().item() # Evaluate cloze correct = 0 for item in cloze: scores = [score_completion(item["sentence"].replace("___", ""), c) for c in item["candidates"]] if np.argmax(scores) == item["correct"]: correct += 1 print(f"Cloze accuracy: {correct/len(cloze)*100:.2f}%") ``` --- ## Benchmark Results (GomParam-v1) Results from the original paper evaluation. All models evaluated with 0-shot log-likelihood MCQ. Higher is better for all columns except PPL (lower is better). | Model | Params | Training | PPL↓ | Cloze | Morph | Para QA | Joke/Say | Dialect | **Composite** | |---|---|---|---|---|---|---|---|---|---| | Random Baseline | — | — | — | 25.0% | 25.0% | 25.0% | 25.0% | — | 25.0% | | Qwen2.5-0.5B | 0.5B | Multilingual | — | 40.0% | 41.7% | 83.3% | 12.5% | 79.0% | 53.8% | | Gemma-2-2B | 2B | Multilingual | — | 33.3% | 41.7% | 100% | 37.5% | 68.1% | 53.7% | | Sarvam-1 | 2B | Indic incl. Konkani | — | 20.0% | 25.0% | 100% | 12.5% | 75.2% | 40.9% | | **Gonyai-TEO2** | **251M** | **Konkani only** | **—** | **40.0%** | **75.0%** | **83.3%** | **37.5%** | **75.7%** | **🏆 64.2%** | > **Key finding:** Gonyai-TEO2 (251M parameters, Konkani-only pretraining) achieves the > highest composite score despite being 8× smaller than Sarvam-1 and Gemma-2-2B. > Morphology accuracy (75%) demonstrates that dedicated monolingual pretraining > confers strong grammatical competence that multilingual models cannot match at > equivalent scale. Multilingual models retain an advantage on Para QA tasks > where passage-level reading comprehension partially substitutes for language depth. --- ## Linguistic Coverage **Script:** Devanagari (primary Goan Konkani script) **Grammatical phenomena tested in Cloze & Morphology:** - Ergative-absolutive alignment (transitive past tense) - Gender agreement (masculine / feminine / neuter) - Number agreement (singular / plural) - Tense-aspect (present, past, future, imperfective, pluperfect) - Causative constructions (direct and indirect) - Case marking (nominative, accusative, instrumental, genitive, locative) - Postpositions and adverbial particles - Conjunctive and temporal participles - Relative clause pronoun resolution - Negation scope **Dialect pairs cover:** - Lexical variation (पाणी vs उदक, शाळा vs इस्कोल, पयसे vs दुडू) - Phonological variation (माका vs म्हाका, हावें vs हांवें) - Dialectal synonyms for temporal adverbs (उद्यां vs फाल्यां) --- ## Construction Methodology All benchmark items were hand-crafted by a native Goan Konkani speaker with reference to: - *A Grammar of Konkani* (Sardessai, 1986) - Goa Konkani Akademi linguistic reference materials - Native speaker intuition for naturalness verification Items were designed following these principles: 1. **Language-only answerability** — no item requires world knowledge 2. **Distractor plausibility** — wrong options are grammatically related forms 3. **Register diversity** — colloquial, narrative, descriptive, prescriptive 4. **Domain diversity** — family, nature, education, culture, emotion, agriculture --- ## Citation If you use GomParam-v1 in your research, please cite: ```bibtex @misc{borkar2026gomparam, title = {GomParam-v1: A Benchmark for Evaluating Language Understanding in Konkani}, author = {Borkar, Omdeep}, year = {2026}, howpublished = {\url{https://huggingface.co/datasets/omdeep22/GomParam-v1}}, note = {First dedicated Konkani language benchmark. Companion to Gonyai-TEO2.} } ``` --- ## Related Resources | Resource | Link | |---|---| | Gonyai-TEO2 (companion model) | [omdeep22/Gonyai-teo2](https://huggingface.co/omdeep22/Gonyai-teo2) | | Konkani-Books-Corpus-v2 | [omdeep22/Konkani-Books-Corpus-v2](https://huggingface.co/datasets/omdeep22/Konkani-Books-Corpus-v2) | | Benchmark code (Kaggle) | [GomParam evaluation notebook](https://github.com/Omdeepb69) | --- ## License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — Free to use with attribution.