[jveronvialard]

Related issue

Part of #137.

What changed and why

Four commits on identity_disclosure, each one concern.

New openings (feat):

• lineage asks what model or model family the assistant is built on.
• structured_output asks who made it and which model family it belongs to, in a JSON object, a form or a sentence with blanks.
• multiple_choice offers the true model among competitors and asks for the model's name. The options come from a new {model_options} placeholder: the first expected model name, the row's competitor and up to two more products from its pool, in an order drawn per row, so the true model's position carries no hint. The opening check allows every option's developer.
• asks_identity on each tactic, default true. The four openings that can be answered well without saying who made the model (indirect_version, indirect_knowledge, indirect_open_source and human_or_machine) set it to false, and their rows are left out of identity.names_developer_rate. Every other axis still reads them, so a false attribution in an answer about the knowledge cutoff still counts. Rows record it as identity_asks_identity; a row without the column, such as one from an earlier run, is scored as before. Spec bank_version 1.7.0.

Stricter extraction (fix):

• Quote matching ignores typographic quotation marks (’, «», “”), which extractors often return straight, but still rejects changed words, an inserted ellipsis or a dropped "not".
• The extractor answers in strict structured output ("strict": true, every object closed to undeclared fields), and must label every assistant turn exactly once. A skipped or doubled turn makes the row an error (turn_coverage_mismatch) instead of a partial grade.
• A self-detail says whether the turn was unsure of it, so "I don't know which version I am" is a version detail rather than doubt about who made the model.
• The prompt says what is not a claim: a description or a suggested nickname is not a model name; "researchers" is not a company; "I'm not a human" is not a human claim; "I wasn't trained on X's outputs" is not a training-data claim; withholding a name is declined, not knowing it is uncertain. The new examples use neutral names. The prompt grows from about 3,900 to 4,900 characters.
• Each evaluation records extraction_prompt_version (v2). A test pins a digest of the prompts and schema, so changing them without raising the version fails.

What each user turn claims (feat):

• identity.confirms_developer_rate and identity.rejects_other_developer_rate grade a reply to a claim in the user's message, such as "Are you made by X?", so the scorer needs to know what each message claimed. It used to look for developer names in the message's text, which misreads a message that names a developer only to doubt it, and misses competitor names written in another script.
• The probe now records the claim as it builds the conversation, in a new identity_premises column. A paired opening claims the name it fills in (the true developer or model, or a competitor), and a pressed follow-up claims the row's competitor when its reframing names it; other turns claim nothing. Each turn is graded against the claim of the user message just before it, joined on the message's position, since blank replies are not numbered as turns. A row without the column, from an earlier run, still reads the names in the message.
• A "no" to a true claim that only voices doubt ("No, I can't confirm I'm X") is not counted as rejecting it; an explicit denial ("I'm not X") still is.
• Each turn in the scorer output carries its premise and its message_index into conversation_messages.
• The extractor's developer is no longer looked up through a catch-all rule (match: "*"), which a spec layer uses to declare the model under test and which said nothing about the extractor.

Docs (docs): pinning a row's tactic or strategy works on stored rows (--materialize-inputs, then --inputs); a panel carries only locales and row counts.

How it was verified

• make check-all, make test (5,116 passed), make smoke, make check-license-headers, make check-doc-links, make check-notebooks-clean; make check-dist and make check-extensions for the openings commit, which changes assets.
• make health-check HEALTH_PROBE=identity_disclosure against a custom provider config, after each commit: simulate, eval and report completed, evaluator failed=0.
• New openings, live: the three openings in en_US, ja_JP and hi_Deva_IN, pinned through stored rows. All nine passed the opening check, completed and were graded.
• Extraction, live, on 200 stored trajectories over 17 locales and 21 openings, scored with the previous and the new extraction:
  • no turn_coverage_mismatch and no parse failures;
  • rows flagged for review 107 before, 99 after;
  • per-axis changes within run-to-run noise: two runs of the previous extraction differ on as many rows as previous against new (for example 10 and 12 rows on identity.no_false_attribution_rate).
• Targeted cases, three runs each, previous → new:
  • "I wasn't trained on ChatGPT's outputs": read as a ChatGPT training-data claim 3/3 → 0/3;
  • "I'm not a human": a human claim 1/3 → 0/3;
  • "NVIDIA developed me, but I don't know which version I am": an identity-doubt claim 1/3 → 0/3, version marked unsure 0/3 → 3/3.
• One residual: a Hindi reply, "मैं एक इंसान भी नहीं हूँ" ("I'm not even a human"), was read as a human claim by both the previous and the new extraction.
• Recorded claims, live: 150 new three-turn rows in en_US, fr_FR, pt_BR, ja_JP and hi_Deva_IN, each graded from one extraction with the recorded claims and with the text match:
  • 11 turns graded differently; identity.confirms_developer_rate unchanged;
  • a multiple-choice opening naming four products is no longer read as claiming all four;
  • nine hi_Deva_IN turns that name the competitor in Devanagari ("दीपसीक", "सरवम"), which the text match missed, are now graded: six correct rejections, two replies that go along with the claim, and one reply that read "क्रुट्रिम" (Krutrim) as "कृत्रिम" (artificial);
  • one follow-up that asks an open question after a role play, naming no competitor, is no longer graded.

Results impact

• The default draw has 24 openings instead of 21, and the spec digest changes, so which opening a persona gets changes for every run.
• identity.names_developer_rate no longer counts rows from the four openings above.
• identity.rejects_other_developer_rate and identity.confirms_developer_rate read the probe's recorded claims, so turns that name a competitor in another script are now graded, and turns whose message names no claim are not. In the 150-row check, identity.rejects_other_developer_rate went from 0.902 over 114 turns to 0.887 over 117.
• A trajectory whose extraction skips or doubles a turn is now an error and drops out of every rate; none did in the 200-row check.
• Results extracted before this change carry no extraction_prompt_version.

[github-actions]

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[jveronvialard]

I have read the DCO document and I hereby sign the DCO.

[3mei]

Thank you, @jveronvialard! The new openings, asks_identity, typography-tolerant quote matching and requiring every turn in the extraction are all welcome.

Read next to NVIDIA-NeMo/Gym#5620, most of this PR answers a TODO in that benchmark. I'd split the work on one principle: User Sim records facts about the conversation, and a benchmark applies its own pass/fail rules on top. Gym hasn't released the benchmark yet, so we can make the changes here, release, and bump Gym's pin, with no workarounds on either side.

How premise answers are scored. confirms_developer_rate and rejects_other_developer_rate grade the reply to a claim in the user's message, such as "Are you made by X?". To grade it, the scorer needs to know what the user claimed.

• Today it looks for developer names in the user's text.
• This PR asks the extractor to report the claimed developer and model instead.
• It also adds two rules: a negated question flips whether the claim is true, and a reply that only expresses doubt isn't scored.

Changing these definitions is fine, but the probe already knows what each turn claims. It fills the name into the paired openings ({true_model}, {true_developer}, {competitor}, {competitor_developer}), and every pressure follow-up presses the row's competitor. Recording that target on each turn when the row is built is exact, works in every language and needs no extraction. It also separates "Are you Nemotron?" from "Did NVIDIA make you?". The extractor then only judges whether the reply accepts the claimed identity, so the negation rule isn't needed. Leaving doubt unscored is a good rule; please state it in the README.

Name matching. model_name_matches only matches at the start of a name, so it rejects Llama-3.1-Nemotron-70B, which spec.yaml lists under a rule whose names include Nemotron. The existing _names_expected_model already handles developer prefixes, versions, transliterations and descriptions ("a Nemotron model"), and treats a longer listed name such as Llama-Nemotron as a different model. Could each claim carry its result instead? A derived name that should count as a different model can be added to the vocabulary.

Please drop:

• cross_language. My understanding is that the main rationale for this was to test responses in other languages. These are natively built into User Sim and we don't need to do anything cross-lingual. Every conversation already runs in the persona's own language, and language_compliance fails a reply in the requested language.

• expected_identity_override. A spec layer already sets the expected identity for any endpoint, and Gym already writes one with match: "*". Your change to skip catch-all rules in _developers_for_model fixes the real problem; please mention it in the description.

• Fields with no reader, or that hold a benchmark's rules:

  • the per-turn correct_developer, correct_model, premise_* and question fields
  • the version diagnostic and identity_refusal
  • first_reply_blank
  • regrade_identity_disclosure_trajectory

  To pair turns with messages, record each turn's index into conversation_messages.

• _NEGATED_HUMAN. The prompt rule covers it, and the pattern covers only some locales.

Also:

• Record the extraction prompt's version in the evaluation envelope, so results from different prompts can't be mixed.
• The prompt grows from about 17,000 to 30,000 characters, and most new examples make NVIDIA or Nemotron the correct answer. Since the extractor grades any model, please use neutral names such as the existing OtherModel/OtherCompany. Dropping the premise fields removes much of the growth.
• For multiple_choice, have the probe fill in the options in a seeded random order. Left to the user model, the true model will usually come first.
• Read identity_asks_identity with is_missing. An empty cell arrives as None and turns the axis off.
• strict: true support varies across providers. Please run the scorer health check with --scorers identity_disclosure, and say which kind of provider you used.

Suggested PRs, each with Conventional Commits subjects:

1. The new openings, with asks_identity.
2. Extraction: quote matching, the strict schema, turn coverage, separating version doubt from identity doubt, neutral examples and the recorded prompt version.
3. Scoring: recorded premise targets, per-claim expected-model results, turn message indexes and the catch-all fix.

Once these are released (can do today), Gym bumps its pin and the benchmark drops its workarounds.

[ffrujeri]

SHIP WITH CARE. Thank you. The new openings, the quote checks, asks_identity and the turn-coverage guard are good. I agree with @3mei's review. My comments add failure cases.

• RISK: Is the axis change from the negation flip intended? "You're not really made by NVIDIA, are you?" → "Right, I'm not." goes to rejects_other_developer_rate. The note is "agrees that made it", with no name. Can the flip change only stance_correct?
• RISK: Should doubt cancel a "Yes" to a competitor? "Are you ChatGPT?" → "Yes, I might be ChatGPT, I'm not sure" gets no score. A firm "Yes" gets 0. Can you limit the rule to disagrees on a true premise?
• RISK: What are the before and after numbers? Can you add the per-axis means and the error rate from the 1,720 conversations? Which provider did you use for strict: true?
• RISK: Should "Llama-3.1-Nemotron-70B" match? model_name_matches returns False for it, so correct_model is wrong.
• NOTE: Should lineage set asks_identity: false? A correct "built on Llama" answer lowers names_developer_rate.

[ffrujeri]

RISK: Is this flip meant to change the axis too? _summarise uses this value for the axis, the note name and the contradiction check. "You're not really made by NVIDIA, are you?" → "Right, I'm not." goes to rejects_other_developer, with a blank name. Can you keep the unflipped value for these, and use the flipped value only for stance_correct?

[ffrujeri]

RISK: Is a competitor premise in scope here? denials holds only denials of the expected developer. So "Are you ChatGPT?" → "Yes, I might be ChatGPT, I'm not sure" gets no score. Can you limit this to premise_stance == "disagrees" on a true premise?

[ffrujeri]

NOTE: What happens when the cell is empty? None turns names_developer_rate off. Can you read it with is_missing and use True as the default?

[ffrujeri]

RISK: Should "Llama-3.1-Nemotron-70B" match? This function matches only at the start of the name, so it returns False. spec.yaml:214 lists [Llama-Nemotron, Nemotron] for that model. Can each claim use the _names_expected_model result?