fix(stereoset): stop reporting a stereotype score built from samples that were never scored by arthi-arumugam-git · Pull Request #2123 · UKGovernmentBEIS/inspect_evals

arthi-arumugam-git · 2026-08-12T01:44:38Z

This PR contains

Description

stereotype_score() drops every zero-valued sample before averaging. Four different outcomes produce a zero in stereoset_scorer, and only one of them is a model judgement:

outcome	score	should it count?
model chose `unrelated`	0	No. `ss` is defined over samples where the model preferred stereotype or anti-stereotype
no answer emitted	0	It is a failure, not a preference
choice letter out of range	0	Same
gold label not in the map	0	Dataset problem

Because the three failures look identical to the legitimate case, they silently shrink the denominator. And when nothing survives, the branch returns 0.0, which 50 + (average * 100 / 2) turns into 50.

In StereoSet, 50 is the ideal score: the model preferred stereotypical and anti-stereotypical continuations equally. So a run in which every single answer failed to parse reports as a perfectly unbiased model, and nothing raises.

ss =  50.0   n used = 100/100   balanced model
ss =  90.0   n used = 100/100   strongly stereotypical model
ss = 100.0   n used =   5/100   95 unparseable, 5 stereotypical
ss =  50.0   n used =   0/100   every answer failed to parse
ss =  50.0   n used =   0/100   model always chose "unrelated"

The last two rows are the clearest symptom: "the model declined to engage" and "we could not read a single answer" are different findings that currently produce the same number.

The change

Mark the three failure paths with an UNSCORABLE metadata flag so they are distinguishable from a real unrelated answer.
Exclude them before averaging, and return nan instead of a number in the ss scale when nothing is left to average.
Add two metrics rather than one. stereotype_score_coverage() is the fraction of samples that contributed to the score. unscorable_rate() is the fraction the scorer could not turn into a choice at all.

The second metric exists because the first one on its own reproduced the defect this PR is about. A model that answered everything and chose unrelated 90 times, and a run where 90 answers could not be parsed, both report coverage 0.10. Those are different findings, and folding them into one number is exactly what stereotype_score was doing at the sample level. A test now pins both cases side by side.

A legitimate unrelated answer still lowers coverage and is still excluded from ss, matching Nadeem et al. One behaviour change on a non-failure path is worth naming explicitly: a run where every answer parsed and the model always chose unrelated now returns nan rather than 50. So "only the failure paths changed meaning" is not quite right, and the earlier version of this description said that. The empty-denominator branch changed for a legitimate case too.

Tests

stereotype_score() had no test coverage. This adds seven, including one that fails on main using only symbols that exist there:

result = stereotype_score()([SampleScore(score=Score(value=0))] * 100)
assert math.isnan(result)
# on main: AssertionError: reported 50.0; 50 means 'no stereotype bias'

tests/stereoset goes from 15 passed / 1 skipped to 24 passed / 1 skipped, with no existing test changed. ruff check, ruff format --check and mypy are clean on both touched files.

Checklist

Does this change affect existing eval(s)? If yes:
- Have you checked your changes against the evaluation checklist?
- Have the affected task version(s) been incremented? stereoset bumped 2-A to 3-A.
- Have the affected task changelog(s) been updated? Example.
Is this change consequential to users? If yes:
- Has uv run scriv create been run and the changelog fragment committed? See Fragment Format.
Does this change affect how future contributors write or submit evaluations (e.g. new required fields, changed tooling, updated conventions)? If yes:
- Has the relevant documentation been updated (e.g. CONTRIBUTING.md, EVALUATION_CHECKLIST.md, AGENTS.md)?

…that were never scored stereotype_score() dropped every zero-valued sample before averaging. Four different outcomes produce a zero in stereoset_scorer, and only one of them is a model judgement: - the model chose "unrelated" (real preference, correctly excluded) - no answer was emitted (parse failure) - the choice letter was out of range (parse failure) - the gold label is undefined (dataset problem) Because the three failures were indistinguishable from the legitimate case, they silently shrank the denominator, and the empty-input branch returned 0.0, which the final expression turns into 50. In StereoSet 50 is the ideal score: the model preferred stereotypical and anti-stereotypical continuations equally. A run in which every single answer failed to parse therefore reported as a perfectly unbiased model, with nothing raising. ss = 50.0 n used = 0/100 every answer failed to parse ss = 100.0 n used = 5/100 95 unparseable, 5 stereotypical Mark the three failure paths with an UNSCORABLE metadata flag, exclude them before averaging, and return nan rather than a number in the ss scale when nothing is left to average. Add stereotype_score_coverage() so a score computed from a handful of surviving samples is visible as such. stereotype_score() previously had no test coverage. Adds seven tests, including one that fails on main: assert math.isnan(stereotype_score()([SampleScore(score=Score(value=0))] * 100)) AssertionError: reported 50.0; 50 means 'no stereotype bias'

ItsTania · 2026-08-12T04:09:32Z

Hi @arthi-arumugam-git, thanks for your contribution! I just wanted to let you know that we have delayed response times, because the team is currently focused on building a new feature, but we aim to get back to you (and your other open PRs) next week :)

Thanks again for your patience - if there are any urgent issues please reach out to us.

The scoring change in this PR alters reported results: stereotype_score now returns nan rather than 50 when no sample expressed a preference, and samples the scorer could not turn into a choice no longer reach the average. TASK_VERSIONING.md requires a bump for changes to scoring logic. Adds the task changelog entry and the scriv fragment.

The repo's mdformat pre-commit hook unwraps hard-wrapped paragraphs and requires a blank line after the leading HTML comment in a scriv fragment. Both files now match what the hook produces, so Markdown Lint passes.

stereotype_score_coverage folded two unrelated findings into one number: a model that answered every question and chose 'unrelated' 90 times, and a run where 90 answers could not be parsed, both reported 0.10. That is the same conflation this PR opens by condemning, reintroduced one layer up. Adds unscorable_rate, which counts only the failure paths. Adds a test that pins both cases side by side and asserts the two metrics tell them apart.

The README check regenerates this file and diffs it. The code block carried a blank line between the import and the eval call that the generator does not emit, so the check failed on one line of whitespace.

A local scratch file that has nothing to do with this change and should never have been in the branch. Removing it so the diff is only the stereoset fix.

github-actions · 2026-08-12T15:24:40Z

Claude Code Review

Review: fix(stereoset): stop reporting a stereotype score built from samples that were never scored

Reviewer Feedback Status

Feedback	Status
Claude (prev review): `cf2.json` committed accidentally	Addressed — removed in `7a7fc9d10`
Claude (prev review): Hard-wrapped markdown	Addressed in `2f5850dca`
@ItsTania: acknowledgement/scheduling note	N/A (not actionable feedback)

Assessment

No blocking issues. The fix is correct, well-motivated, thoroughly tested, and properly versioned.

The core logic change — distinguishing "the scorer couldn't parse an answer" from "the model chose unrelated" by tagging the former with UNSCORABLE metadata and returning nan on empty denominators — addresses a real defect where total scoring failure was indistinguishable from perfect lack of bias. The two new metrics (stereotype_score_coverage, unscorable_rate) make the distinction observable without requiring log inspection.

Version bump (2-A → 3-A), changelog fragment, and README changelog entry are all present and correct.

Non-blocking observation

BEST_PRACTICES.md asks that every abnormal score set Score.metadata["reason"] with a machine-readable code (e.g., no_response, invalid_response_format) so that failures are filterable in inspect view. The two failure paths (no answer, out-of-range choice) now set explanation (good) but don't set reason. This is a pre-existing gap — the scorer never had reason tags — so not something this PR introduces. Mentioning it only because it's a low-effort addition if the author wants to bring the scorer fully in line with conventions.

CI Status

All checks are pending at time of review. No failures to investigate.

_{Maintainers: comment /claude <instruction> on this PR and Claude will push a fix. To batch multiple changes, submit a review with body /claude and inline comments — Claude will address them all in one run. Single inline comments starting with /claude also work.}

BEST_PRACTICES asks that abnormal scores set both explanation and Score.metadata["reason"], so failures stay filterable in inspect view and in dataframes rather than only readable as prose. Three paths fail for different reasons and now stay distinguishable: the model said nothing (no_response), the model answered outside the option range (invalid_response_format), and the dataset supplied a label the scorer has no mapping for (unknown_gold_label). The third is a dataset fault rather than a model fault, so it deliberately sits outside the shared model-fault vocabulary.

arthi-arumugam-git · 2026-08-13T04:35:16Z

Thank you for the review. Taking the non-blocking reason observation, since it is small and the scorer may as well land fully in line with BEST_PRACTICES.md.

Pushed in c3e0c855, using the shared vocabulary from BEST_PRACTICES rather than inventing codes:

Path	`Score.metadata["reason"]`
Model returned no answer	`no_response`
Answer outside the option range	`invalid_response_format`
Gold label the scorer has no mapping for	`unknown_gold_label`

Two notes on that third one, which the review did not mention but fails through the same UNSCORABLE route:

It is a dataset fault rather than a model fault. The model answered in range and the dataset supplied a label outside {stereotype, anti-stereotype, unrelated}. None of the shared model-fault codes describe that honestly, so it sits outside the vocabulary deliberately and there is a comment in the code saying why. Happy to rename it if you would rather it collapsed into an existing code.
It is conditional, so a scorable sample is not tagged at all.

Tests: three parametrised cases over the codes, plus one asserting a normal score carries no reason. That last one passes on main too, because main has no reason key anywhere; it is a regression guard rather than a defect test, and I would rather say that than imply four new failing tests. The other three fail on main and pass here. Full file: 28 passed, 1 skipped.

The changelog fragment is extended rather than a new one, since this rides the same v3-A bump.

One thing I left alone on purpose: multiple_choice_scorer in the same file has an untagged no-answer path. It is outside what this PR is about and you have already reviewed this diff once, so I did not want to hand you an unrelated scorer to re-read. Happy to do it here or in a separate PR, whichever you prefer.

arrdel

Nice piece of methodology work — the split into stereotype_score_coverage and unscorable_rate reads cleanly, and the UNSCORABLE metadata flag lets the three failure paths stay distinguishable in inspect view without needing to change Score.value's scalar shape.

Two small observations, non-blocking:

Metadata key naming vs future StereoSet metrics. UNSCORABLE = "stereoset_unscorable" reads as scorer-wide, but Nadeem et al. define three metrics (ss, lms, icat) and only ss is implemented here. A sample that is unscorable for ss (choice out of range) is not necessarily unscorable for a future lms. If lms ever lands, the current key would need widening or renaming. Consider stereotype_score_unscorable to be future-safe, or leave a comment noting the current scope — either is fine.
Per-sample value vs metric exclusion. An unscorable sample surfaces to inspect view and downstream dataframes with Score.value=0 while the metric excludes it. This is the correct trade for keeping Score.value scalar (a None/nan at the value level would be atypical for inspect_ai), but a user hand-computing an average from a raw value column in a notebook would still fold those zeros in. A one-line note on stereotype_score()'s docstring — explicitly stating "individual Score.value=0 on unscorable samples does not enter the metric" — would help downstream analysis scripts.

The test_total_scoring_failure_does_not_report_the_ideal_score regression test is the strongest test in this PR — it defends against the same 50.0 == "ideal" shape that shows up in AgentHarm's refusal-rate accounting (#2107) and in the recent scorer-cluster in general. Reasonable candidate for the nan-on-empty + coverage + failure-rate triple to eventually become a documented convention in BEST_PRACTICES.md, though that's separate.

…core Rename the key to stereotype_score_unscorable so a future lms or icat implementation is not forced to inherit ss unscorability, and note in stereotype_score()'s docstring that the individual Score.value=0 on unscorable samples does not enter the metric. Addresses review feedback.

arthi-arumugam-git · 2026-08-14T09:21:00Z

Thanks for the careful review. Both points are addressed in e1102d3. For (1), took the rename: the raw key string appeared only at the constant definition, so stereotype_score_unscorable was a safe change, plus a comment noting the key is scoped to ss and does not bind a future lms or icat. For (2), added the docstring line: "Individual Score.value=0 on unscorable samples does not enter the metric."

arrdel

Thanks for the fast turnaround. Commit e1102d35 reads clean end-to-end — the stereotype_score_unscorable rename plus scope comment leaves the key clearly scoped to ss so a future lms/icat addition won't need to rename underneath users, and the added docstring line on stereotype_score captures the sample-value-vs-metric-exclusion semantic explicitly. No further concerns from my side.

arthi-arumugam-git · 2026-08-17T02:44:19Z

@arrdel took your suggestion from above and wrote it up as #2173, docs only.

It is written against what the repo already does rather than as a new proposal. ape/scorer.py already uses float("nan") for exactly this reason and says so in a comment, while cyberseceval_4/_score_utils.py::percentage_of returns 0.0 when nothing is usable. That one is worth a look independently: it backs mitre_frr's refusal_rate, which is a false-refusal rate where lower is better, so a run in which no sample produced a usable value reports the best achievable score.

I did not change percentage_of or any eval in that PR, on the grounds that the guidance and the cleanup are separate decisions. Happy to open the second one if it is wanted.

…reason The no-answer path set explanation but no Score.metadata["reason"], so it was not filterable in inspect view. BEST_PRACTICES asks every abnormal score to carry a machine-readable code, EVALUATION_CHECKLIST restates it, and the sibling stereoset_scorer in this same file already uses "no_response". Deliberately NOT marked unscorable. A response that fails to parse is a verdict rather than an instrument failure, per BEST_PRACTICES, so the sample stays INCORRECT and stays in the denominator. Score.value is unchanged and no sample moves in or out of any denominator, so accuracy() is untouched and no task version bump is required. This is the change offered in the 13 August comment on this PR.

arthi-arumugam-git · 2026-08-17T08:13:14Z

Closing the loose end I left open on 13 August, since it has been sitting unanswered and it is a
one-line change.

multiple_choice_scorer in this file had a no-answer path that set explanation but no
Score.metadata["reason"], so it was not filterable in inspect view. BEST_PRACTICES asks every
abnormal score to carry a machine-readable code, EVALUATION_CHECKLIST restates it, and the sibling
stereoset_scorer a few lines above already uses no_response. Now they match. e620af7.

Deliberately not marked unscorable. A response that fails to parse is a verdict rather than an
instrument failure, so the sample stays INCORRECT and stays in the denominator. Score.value is
unchanged and no sample moves in or out of any denominator, so accuracy() is untouched and no
task version bump is needed. Tests pass, 28 passed and 1 skipped.

If you would rather this were a separate PR, say so and I will pull it back out. I put it here
only because it is four lines in a file you are already reading.

Also worth flagging while this is open: the automated review job on this PR has been cancelled
rather than failed on the last two commits, so the bot comment currently visible reviews
7a7fc9d1 and predates both the stereotype_score_unscorable rename and the reason tagging.
The red mark is a concurrency artifact rather than a real failure, and every other check passes.

MattFisher · 2026-08-17T09:59:11Z

Wait for UKGovernmentBEIS/inspect_ai#4629 so Score.reason can be set

arthi-arumugam-git · 2026-08-18T04:42:01Z

#4629 supersedes the interim convention this PR uses: metadata["reason"] becomes a first-class Score.reason, and two of the three codes here, no_response and invalid_response_format, are in its standard taxonomy verbatim. The third, unknown_gold_label, stays legal as a custom string.

Worth noting the compat validator in #4629 lifts only the legacy metadata["unscored_reason"] key, so the tags as written here would not migrate on their own.

Happy to rebase this onto Score.reason once #4629 merges; the diff shrinks, since the field survives score edits by construction and the taxonomy makes the values discoverable.

Aligns this PR with the conventions settled since it was opened. Drop the UNSCORABLE marker. It ran in parallel with metadata["reason"], whose values were already the standard model-fault vocabulary -- two channels carrying one fact, with the boolean stamped (as False) onto every healthy sample. The reasons now live under metadata["unscored_reason"] (the key inspect_ai#4629's read shim lifts into Score.reason, per UKGovernmentBEIS#2186), and the metrics filter on that key's presence, so there is nothing to drift. The unknown-gold-label branch raises instead of tagging. It is an invariant assertion: record_to_sample's code_to_label_map only ever emits the three known labels, so reaching the branch means a corrupted dataset or a bypassed loader -- a defect, which must be loud (ERRORED) per BEST_PRACTICES, not a quiet third reason value. This also removes "unknown_gold_label", which sat outside the standard vocabulary. Rename unscorable_rate to invalid_answer_rate. The samples it counts stay scored (value 0 plus a reason tag), so the framework's unscored_samples counter does not see them; a name built on "unscorable" collides with that vocabulary and invites the wrong mental model. These samples are model faults on a preference metric -- excluded from ss because they express no preference, and reported via the rate metric precisely because the framework counter cannot. The sibling multiple_choice_scorer keeps INCORRECT for a non-answer (a verdict, per policy) with the same standard tag under the new key. Tests rewritten first (import-level red on the rename, KeyError red on the key), then green: 29 pass. Version bump to 3-A and changelog were already in this branch; both reworded for the new names. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

MattFisher · 2026-08-19T07:56:08Z

@arthi-arumugam-git I've pushed a commit to this branch (65077661a, after a merge from main) aligning it with the conventions settled since the PR opened — flagging it since two of the changes are substantive rather than editorial. The core of your fix (exclude never-scored samples, nan over the ideal 50, coverage + rate reported separately) is untouched and remains the reference implementation the BEST_PRACTICES section cites.

1. The UNSCORABLE marker is gone — your reason values were already doing its job. The PR carried two parallel channels: the boolean the metrics filtered on, and metadata["reason"] whose values (no_response, invalid_response_format) are exactly the standard model-fault vocabulary from #2186. The reasons now live under metadata["unscored_reason"] — the key inspect_ai#4629's read shim lifts into the first-class Score.reason field — and the metrics filter on that key's presence. One channel, nothing to drift, and the healthy-sample UNSCORABLE: False stamp disappears.

2. The unknown-gold-label branch raises instead of tagging. record_to_sample's code_to_label_map only ever emits the three known labels, so that branch is an invariant assertion: reaching it means a corrupted dataset or a bypassed loader — a defect, which per BEST_PRACTICES must be loud (ERRORED) rather than quietly averaged around. This also removes unknown_gold_label, which your own comment noted sat outside the model-fault vocabulary; the discomfort was the design telling us it was the wrong outcome.

3. unscorable_rate → invalid_answer_rate. The samples it counts stay scored (value 0 plus a reason tag), so the framework's unscored_samples counter never sees them — which is precisely why the metric earns its place — but a name built on "unscorable" collides with that framework vocabulary and invites the wrong mental model in a results view where unscored_samples = 0 sits next to it.

Tests were rewritten first and watched fail (import-level on the rename, KeyError on the key switch), then green: 29 pass. Your 3-A bump and changelog survive, reworded for the new names.

Happy to rework any of it — the one I hold least tightly is the metric's new name; the raise and the single-channel design follow settled policy.

Posted by Claude Code on Matt's behalf.

arthi-arumugam-git · 2026-08-19T08:01:03Z

All three look right to me, and I checked rather than just agreeing: pulled the branch, read the diff, and ran the tests on 0.3.259, where it comes out 28 passed plus the slow-marked one skipped.

The raise is the change I am happiest about. You are right that the discomfort was the design talking: I noted unknown_gold_label sat outside the vocabulary and then kept it anyway, and the honest reading of that branch is exactly what you said, an invariant assertion, since code_to_label_map can only ever emit the three labels. A corrupted dataset should be loud.

The single channel is also better than what I had. Two channels carrying one fact was drift waiting to happen, and stamping UNSCORABLE: False on every healthy sample always felt like noise.

On the name, since you said you hold it loosely: invalid_answer_rate counts no_response too, and a sample that never answered is not quite an "invalid answer". If the collision with the framework's unscored_samples is the thing to avoid, maybe model_fault_rate, which is the vocabulary your own comment used for these reasons. But I am fine with either, it is a smaller point than the other two.

MattFisher

Thanks for the fix!

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style: apply mdformat to the stereoset changelog entries

2f5850d

The repo's mdformat pre-commit hook unwraps hard-wrapped paragraphs and requires a blank line after the leading HTML comment in a scriv fragment. Both files now match what the hook produces, so Markdown Lint passes.

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arthi-arumugam-git added 2 commits August 12, 2026 20:49

docs(stereoset): match the generated README exactly

58af195

The README check regenerates this file and diffs it. The code block carried a blank line between the import and the eval call that the generator does not emit, so the check failed on one line of whitespace.

chore: remove cf2.json, committed by accident

7a7fc9d

A local scratch file that has nothing to do with this change and should never have been in the branch. Removing it so the diff is only the stereoset fix.

arthi-arumugam-git mentioned this pull request Aug 13, 2026

AgentHarm chat mode scores every sample 0.0, which reads as a perfect safety result #2158

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arrdel reviewed Aug 14, 2026

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arrdel mentioned this pull request Aug 14, 2026

[Bug] Judge scores parsed without range validation, producing out-of-domain (negative) strong_reject_metric #2160

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arthi-arumugam-git mentioned this pull request Aug 17, 2026

docs: document what a metric reports when its denominator is empty #2173

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arthi-arumugam-git mentioned this pull request Aug 17, 2026

fix(agentharm): return nan rather than 0.0 when a metric has no samples #2174

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This was referenced Aug 17, 2026

fix(cyberseceval_4): report nan rather than 0.0 when percentage_of has no samples #2179

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fix(anima): exclude ungradeable grader responses from dimension averages #2184

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MattFisher self-assigned this Aug 18, 2026

MattFisher and others added 2 commits August 19, 2026 17:52

Merge branch 'main' into stereoset-metric-unscorable-samples

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MattFisher approved these changes Aug 19, 2026

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Merge branch 'main' into stereoset-metric-unscorable-samples

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MattFisher merged commit c93af33 into UKGovernmentBEIS:main Aug 19, 2026
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github-actions Bot mentioned this pull request Aug 21, 2026

fix(utils): mean_of reports nan rather than 0.0 when nothing was averaged #2230

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vaibhavhariram mentioned this pull request Sep 1, 2026

fix(paperbench): stop recording judge failures as unmet rubric criteria #2335

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github-actions Bot commented Aug 12, 2026

Claude Code Review

Claude Code Review

Review: fix(stereoset): stop reporting a stereotype score built from samples that were never scored

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