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What a useful Kannada QA benchmark should measure

A Kannada question-answering benchmark should tell you more than one headline score. It should show whether a model can find the right evidence, extract an answer accurately, handle Kannada linguistic variation, and remain reliable across domains and question types.

This matters because Kannada remains underrepresented in many general-purpose language benchmarks. Results can be distorted by translation artifacts, duplicated passages, inconsistent annotation, or evaluation scripts that treat valid Kannada variants as wrong. Before comparing models, define the task precisely: extractive QA, where the answer is a span in a supplied context; multiple-choice QA; or retrieval-augmented and generative QA, where the model produces an answer from retrieved evidence.

For broader context on dataset selection and evaluation across Indian languages, use this Indian language LLM benchmark datasets guide. A Kannada result is most useful when its data provenance and protocol are clear enough for another team to reproduce.

1. Audit the Hugging Face dataset before modelling

Start by inspecting the dataset card, licence, language labels, collection method, and train-validation-test splits. Do not assume that a repository name such as kannada_qa is available under that exact identifier; dataset names, configurations, and schemas vary. Find the canonical Hugging Face repository and pin a revision for reproducibility.

Install the core tooling:

pip install datasets transformers evaluate accelerate sentencepiece indic-nlp-library

Load and inspect the data:

from datasets import load_dataset

# Replace with the verified repository and configuration.
ds = load_dataset("ORG_OR_USER/DATASET", revision="COMMIT_OR_TAG")
print(ds)
print(ds["train"].column_names)
print(ds["train"][0])

Check the following before training:

• Are questions, contexts, and answers written in Kannada, or are they translated from another language?
• Does each answer include a character or token offset into the context?
• Are there multiple valid answers per question?
• Are examples duplicated across splits or near-duplicated through translated passages?
• Do validation and test sets cover the same domains as training data?
• Are there empty contexts, malformed offsets, code-mixed questions, or non-Kannada samples?

Create a small data-quality report containing row counts, character lengths, domain distribution, script distribution, and duplicate rates. Preserve the original data and document every filtering rule.

2. Establish baselines before fine-tuning

A benchmark needs credible baselines. Start with a simple majority or no-answer baseline where the task permits it, followed by a lexical retrieval baseline such as TF-IDF or BM25. For extractive QA, compare at least one multilingual encoder with a Kannada-capable or Indic-focused model. XLM-R and multilingual DeBERTa are reasonable starting points, but model suitability should be tested rather than assumed.

Keep the comparison fair:

• Use the same train, validation, and test partitions.
• Fix random seeds and record library, CUDA, and model versions.
• Report parameter count, maximum context length, batch size, learning rate, epochs, and hardware.
• Evaluate checkpoints selected only on validation performance.
• Avoid using test examples for prompt design, threshold tuning, or error correction.

A useful comparison can include zero-shot, few-shot, fine-tuned, and retrieval-augmented settings. For teams evaluating several Indic languages, the methodology in benchmarking multilingual LLMs in India provides a helpful structure for separating language performance from general model capability.

3. Normalise Kannada carefully, not aggressively

Exact-match evaluation is highly sensitive to formatting. Build a transparent normalisation function that can address Unicode representation, surrounding whitespace, repeated spaces, and harmless punctuation. Kannada text may contain combining marks, danda-like punctuation, Arabic numerals, Latin words, and code-mixed content. Do not remove characters merely because they are uncommon.

Use Unicode normalisation and compare both raw and normalised answers:

import re
import unicodedata

def normalize_kn(text):
    text = unicodedata.normalize("NFC", str(text))
    text = text.strip().lower()
    text = re.sub(r"\s+", " ", text)
    return text

If you remove punctuation, stop words, or diacritics, state the rule and publish an ablation. For generative QA, add a semantic metric or human review because two answers can be equivalent while differing lexically. For extractive QA, verify that predicted spans are aligned to the context; a semantically correct paraphrase should not be credited under a strict extractive protocol unless the task explicitly allows it.

4. Use task-appropriate metrics

For extractive QA, report Exact Match (EM) and token-level F1. EM measures whether the normalised prediction matches an accepted reference. F1 measures overlap and is more forgiving of partial matches. If the dataset has multiple references, score against the best reference.

For generative or retrieval-augmented Kannada QA, add:

• Answer correctness, assessed by a Kannada-capable evaluator or human annotators.
• Faithfulness, checking whether the answer is supported by the supplied evidence.
• Retrieval Recall@k, measuring whether relevant evidence appears in the top-k results.
• Latency, memory, and cost, especially for deployment in Indian education or public-service settings.
• Abstention quality, when the context does not contain enough information.

Do not use generic classification accuracy as the primary metric for open-ended QA. Report confidence intervals or bootstrap estimates where possible, and break scores down by question type, answer length, domain, and difficulty. Compare performance by script and code-mixing level rather than hiding these cases inside one aggregate number.

5. Build a reproducible evaluation pipeline

For extractive models, use the Hugging Face question-answering pipeline or a Trainer-based evaluation loop, then apply a task-specific post-processing function that maps start and end logits to valid context spans. For generative models, store the exact prompt template, decoding settings, retrieved documents, and model output.

A strong benchmark repository should contain:

• A dataset card or data statement with provenance and licence.
• A pinned dataset revision and model checkpoint.
• Scripts for preprocessing, inference, scoring, and table generation.
• Fixed random seeds and a documented hardware environment.
• Raw predictions, not only aggregate scores.
• A list of excluded or corrupted examples and the reason for exclusion.

Keep a locked test set. If you create a new Kannada evaluation set, use native-speaker review, double annotation for a sample, adjudication rules, and separate challenge cases. Examples should cover formal Kannada, conversational language, regional variation, education, government services, health, and code-mixed queries where relevant.

6. Analyse failures and report results honestly

A score table is only the beginning. Manually inspect false positives and false negatives, grouping them into categories such as wrong retrieval, boundary errors, entity confusion, negation, numerical reasoning, long-context failure, spelling variation, and unsupported generation. Include representative Kannada examples with English glosses only when necessary; preserve the original text for analysis.

Report results in a table with model, training regime, EM, F1, retrieval metrics if applicable, latency, and test-set size. Add confidence intervals and note whether differences are statistically meaningful. If the dataset is small, avoid broad claims about Kannada capability. A model that performs well on news passages may still fail on student queries or government terminology.

For a neighbouring-language comparison, see benchmarking NLP models for Telugu and Sanskrit. If the end goal is an educational product, connect benchmark results to real user needs through AI question-answering apps for Indian students, including safety, citation, accessibility, and escalation requirements.

Recommended 2026 benchmark checklist

Before publishing a Kannada QA result, confirm that you have:

• Verified the Hugging Face dataset identifier, licence, schema, and revision.
• Removed or documented duplicates and leakage across splits.
• Used Unicode-aware Kannada normalisation.
• Reported EM and F1 for extractive QA, plus retrieval and faithfulness metrics where relevant.
• Compared against simple and multilingual baselines.
• Disclosed model, prompt, decoding, training, and hardware settings.
• Published raw predictions and a categorized error analysis.
• Included native-speaker validation for newly created or translated data.

This process produces a benchmark that is useful to researchers, builders, and grant evaluators—not just a number that is difficult to interpret or reproduce.