README.md

---
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.