| # 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 | |
|
|
| ```bash |
| 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. |
|
|
| --- |
|
|
| ## 1. Exact substring |
|
|
| The entire logic: |
|
|
| ```python |
| 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. |
|
|
| --- |
|
|
| ## 2. Word overlap |
|
|
| Both sides are lowercased, split on whitespace, stripped of leading and |
| trailing punctuation (`delhi.` → `delhi`), and compared as **sets**: |
|
|
| ```python |
| 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. |
|
|
| --- |
|
|
| ## 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`. |
|
|
| --- |
|
|
| ## 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. |
|
|
