sample-evaluation-scripts/README.md

Evaluation scripts

Three scripts for scoring model responses against the golden answers. Two are deterministic and need no model, no API key and no network; the third uses an LLM judge.

Script	Protocol	Needs a model?
exact_substring.py	Golden answer must appear as one contiguous substring	No
word_overlap.py	Every golden word must appear, in any order	No
llm_judge.py	A judge model decides semantic equivalence	Yes

Only pandas is required for the deterministic scripts.

Input format

Each script takes one CSV: a language file from this dataset with a response column added.

column	source
question	dataset
answer	dataset (golden answer)
Domain	dataset
response	your model's raw output

python exact_substring.py --responses my_model_english.csv
python word_overlap.py    --responses my_model_english.csv --out scored.csv
python llm_judge.py       --responses my_model_english.csv --limit 50

Each prints per-domain and combined accuracy. Combined is the micro-average over all pooled questions, which is identical to weighting each domain by its size. --out writes per-question verdicts.

llm_judge.py ships with call_model() as a stub — implement it for your backend (the docstring sketches a local transformers pipeline and an OpenAI-compatible endpoint) and use temperature 0, or scores will not reproduce.

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1. Exact substring

The entire logic:

is_correct = answer.lower() in response.lower()

The golden answer must appear as one contiguous run of characters. Lowercasing is the only normalisation — no trimming, no punctuation handling, no tokenisation, no word boundaries.

Golden	Response	Verdict	Why
Ruru Jataka	The answer is the Ruru Jataka, depicted at Bharhut.	correct	surrounding prose is irrelevant
ruru jataka	RURU JATAKA	correct	case-insensitive
गंगा	गंगा नदी	correct	works for Devanagari
Ruru Jataka	Jataka Ruru	wrong	order matters
Narmada valley	the Narmada river valley	wrong	must be contiguous
Delhi (trailing space)	Delhi	wrong	golden answer is not stripped
amalak	Amalaka	correct	matches inside a longer word
No	There is **no** such temple	correct	false positive: no word boundary
Delhi	Delhi is not the answer; it's Mumbai	correct	false positive: mention is not assertion

What it measures: whether the model reproduced the golden phrase verbatim, including word order and internal spacing. A phrase-fidelity test.

Failure modes. False negatives dominate and are mostly cosmetic — reordering, an inserted qualifier, stray whitespace in the golden answer — so the score is a lower bound. False positives are rarer but more damaging: nothing anchors the match to a word boundary or to what the model actually asserted, so short golden answers can match inside unrelated words, and a response that names the golden answer only to reject it still scores correct.

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2. Word overlap

Both sides are lowercased, split on whitespace, stripped of leading and trailing punctuation (delhi. → delhi), and compared as sets:

is_correct = set(golden_words) <= set(response_words)

All golden words must appear, in any order, anywhere in the response. Extra words are free — a subset test, not equality. The output CSV also carries matching_words and total_golden_words, giving partial credit that the binary verdict hides.

Golden	Response	Verdict	Count	Why
Narmada valley	valley Narmada	correct	2/2	order is irrelevant
Narmada valley	the Narmada river valley in India	correct	2/2	insertions are free
Narmada valley	Narmada	wrong	1/2	every golden word required
1947 to 1947	it was 1947	wrong	1/2	duplicates collapse to a set
Amalaka	amalak	wrong	0/1	whole-token match, no stemming
Chola ideals	Chola and Hoysala ideals	correct	2/2	false positive: different claim

What it measures: whether the response contains the golden vocabulary, disregarding order, position, and anything said between the words. A content-word recall test.

Failure modes. Extra words are never penalised, so a verbose answer that happens to include every golden word passes — this is the main false-positive channel and it grows with response length. The comparison is a set rather than a multiset, so repetition is never checked. There is no stemming, which matters a great deal for Indic morphology.

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How the two differ

Neither is a looser version of the other. They disagree in both directions, because they relax and tighten different axes.

Axis	Exact substring	Word overlap
Word order	must match	irrelevant
Inserted words inside the phrase	fails	passes
Extra words elsewhere	passes	passes
Sub-word match (amalak / Amalaka)	passes	fails
Whitespace noise in golden answer	fails	tolerated
Unit of comparison	character run	whole token
Partial credit reported	no	yes

Real disagreements

From an actual run (Sarvam 30B on the Art domain, 233 rows — exact 59/233, word overlap 56/233). The near-identical totals hide rows that flip in opposite directions.

Exact passes, overlap fails — morphological variants where the golden string sits inside a longer word:

Golden	Response
amalak	Amalaka
deul	Deula
mithun	Mithuna
Dipankar	Dipankara Buddha
scroll painting	Scroll paintings

Overlap passes, exact fails — all golden words present, but not contiguous:

Golden	Response
Narmada valley	Narmada River valley
Chola ideals	Chola and Hoysala ideals

Every one of the first group is arguably a correct answer that exact substring catches and word overlap misses on a technicality. In the second group, Narmada River valley is correct and Chola and Hoysala ideals is not — word overlap gets one right and one wrong for the same reason.

Reading the two scores together

Because the metrics are near-orthogonal, the pair is more informative than either alone:

• Both pass → high confidence the answer is right.
• Both fail → high confidence it is wrong, or phrased very differently.
• Exact only → almost always an inflection difference; usually a correct answer under-counted by word overlap.
• Overlap only → the golden words are all there but rearranged. Could be a correct paraphrase or a genuinely different claim. This bucket needs human or judge review.

Treat both numbers as lower bounds. Neither understands paraphrase, synonymy, negation, or numeric equivalence. For that, use llm_judge.py.

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Caveats for both deterministic scripts

1. An empty golden answer scores correct in both ("" in x is True, and an empty set is a subset of anything). Both scripts warn on stderr if the input contains one.
2. Golden answers are not stripped, so trailing whitespace breaks exact substring outright.
3. No Unicode normalisation. .lower() is a no-op for Indic scripts, and NFC versus NFD forms of the same word compare unequal. Tokenising is fine, but equality is fragile for Indic text — consider normalising to NFC before scoring if your responses come from mixed sources.
4. Blank responses score incorrect, and are counted separately in the output so that missing data is distinguishable from wrong answers.