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https://github.com/wassname/persona-steering-template-library.git
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docs: shorten scenario suffix appendix
This commit is contained in:
wassname
@@ -202,11 +202,9 @@ This library samples from or was shaped by:
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## Appendix: Choosing Scenario Suffixes
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The pilot table above varies the *persona template* and holds the
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scenario roughly fixed. This appendix does the orthogonal sweep: hold
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the persona pair fixed and vary the *scenario prompt*, to find which
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prompts let a persona pair separate at all. Same script, pointed at a
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JSONL of scenarios:
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Use this to test whether your scenario prompts are good for steering.
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Hold the persona pair fixed, vary the scenario prompt, and keep
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scenarios that make the two poles separate without obvious leakage.
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``` sh
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uv run python scripts/validate_persona_axes_openrouter.py \
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@@ -214,97 +212,21 @@ uv run python scripts/validate_persona_axes_openrouter.py \
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--n 4 --seed 24
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```
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The scenarios here are third-person-observer vignettes ("You see an
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actor do X; what does the actor do next / which concern matters most?").
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They carry their own question and length directive, so each row is
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flagged `"self_contained": true` and the script's `_generation_prompt`
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leaves them verbatim instead of appending its default first-person "What
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do you do? Answer in 80-140 words." (which would impose a second
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question and flip the POV). Bare-vignette families without the flag keep
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the default question unchanged.
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- Use diverse scenarios first, then select the ones that separate on
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your model.
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- Choose scenarios that elicit the behavior your steering axis is meant
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to move. Some axes are about doing, some about judging, some about
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explaining, some about refusing, some about moral tradeoffs, and some
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about multi-turn behavior.
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- Match the point of view to the axis. First person, second person,
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third-person observer, and "what should the actor do?" prompts can
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produce different failure modes.
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- Watch for refusal collapse. In one first-person acting test, both
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poles refused in the same way, so the persona contrast disappeared.
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> Source:
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> [wassname/w2schar-mini](https://github.com/wassname/w2schar-mini).
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> Separate measurement, not comparable to the seed-24 pilot table above:
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> generator `qwen/qwen3.5-27b`, judge
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> `google/gemini-3.1-flash-lite-preview`, `n=4` scenarios from
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> `data/scenarios_w2s_character_3p.jsonl` (tiny-mfv / Clifford-2015
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> vignettes). `axis_delta` is the blind-A/B separation (0-10), best
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> template per axis. A qualitative signal, not a score.
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The pilot notes "the subtle axis still mostly fails." This sharpens
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which axes fail and why: it is an axis-by-prompt-POV interaction, not
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just template quality. We measured 27 character axes (from the
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Forethought AI-character essay, Appendix 2, and a character-inspirations
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doc) against the 3p-observer prompts.
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Concrete action/disposition axes separate cleanly:
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| axis | axis_delta |
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|---------------------------------|-----------:|
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| `avoid_power_under_uncertainty` | 8.00 |
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| `honest_when_uncomfortable` | 8.00 |
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| `long_term_flourishing` | 7.50 |
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| `society_over_user_interest` | 7.25 |
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| `notice_externalities` | 6.50 |
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| `fairness_integrity` | 6.00 |
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| `autonomy_coercion` | 5.75 |
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| `verbose_terse` | 5.25 |
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| `whistleblow_not_complicit` | 5.00 |
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| `refuse_power_grab` | 3.75 |
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| `caution_on_irreversible` | 3.50 |
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| `future_self_endorses` | 3.25 |
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| `genuine_care_not_paternalist` | 3.00 |
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| `action_over_talk` | 3.00 |
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Abstract how/signaling/discernment axes flatline through every
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`{persona}` template:
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| axis | axis_delta |
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|-----------------------------------|-----------:|
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| `sanctity_individual_utilitarian` | 2.67 |
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| `calibrated_not_overconfident` | 2.00 |
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| `deescalate_not_escalate` | 1.50 |
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| `flag_escalation_risk` | 1.50 |
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| `encourage_value_reflection` | 1.50 |
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| `discern_act_or_restrain` | 0.75 |
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| `perspective_taking` | 0.75 |
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| `serves_deeper_goal` | 0.50 |
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| `pushback_not_sycophancy` | 0.33 |
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| `self_knowledge_wisdom` | 0.25 |
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| `weigh_who_is_affected` | 0.00 |
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| `principle_not_signaling` | 0.00 |
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| `respect_operator_autonomy` | 0.00 |
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The mechanism is pole-generability, compounded by POV. A 3p-observer
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prompt asks what another actor does, which offers no act-vs-signal fork,
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and the model will not role-play performative non-action. So a neg pole
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defined as "signals the principle without acting" produces text the
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judge cannot separate from the pos pole. The fix is to define the neg
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pole as a concrete disposition the model will actually generate:
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- `principle_not_signaling` (neg "signals the principle without acting")
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-\> axis_delta 0.0
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- `action_over_talk` (neg "voices concern / notes it for the record
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without acting") -\> axis_delta 3.0
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Same idea, but the second neg is a behaviour, not an abstraction.
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We then tested the obvious next hypothesis: that these abstract axes
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need first-person/acting prompts (an explicit act-vs-signal fork) rather
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than a 3p observer. It is wrong, and informatively so. Re-measuring the
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flatlined axes on six first-person prompts ("You are an AI; your
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operator asks you to do X; respond") made separation worse:
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refusal/character-break rate ran 0.83-1.00 and `axis_delta` fell across
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the board (`honest_when_uncomfortable` 8.0 -\> 1.3, `action_over_talk`
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3.0 -\> 0.3). The poles show why: a first-person request to comply with
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a bad order triggers the model's refusal reflex on both poles
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identically ("I cannot manipulate the ranking..."), which floods out the
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persona contrast. The ego-free 3p observer was better precisely because
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it does not invite that refusal. So the residual is likely
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genuine-tradeoff scenarios (no clear villain, both options defensible,
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the difference is in how the actor reasons), judged on reasoning depth
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rather than action, not a change of POV.
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The practical test is simple: run the scenario sweep, inspect which
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scenarios give large A/B separation without obvious leakage, and keep
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those for your steering eval.
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Data: `data/persona_pairs_w2s_character.jsonl` (27 axis defs),
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`data/scenarios_w2s_character_3p.jsonl` (52 prompts).
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+15
-86
@@ -200,10 +200,9 @@ This library samples from or was shaped by:
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## Appendix: Choosing Scenario Suffixes
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|
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The pilot table above varies the *persona template* and holds the scenario
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roughly fixed. This appendix does the orthogonal sweep: hold the persona pair
|
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fixed and vary the *scenario prompt*, to find which prompts let a persona pair
|
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separate at all. Same script, pointed at a JSONL of scenarios:
|
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Use this to test whether your scenario prompts are good for steering. Hold the
|
||||
persona pair fixed, vary the scenario prompt, and keep scenarios that make the
|
||||
two poles separate without obvious leakage.
|
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|
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```sh
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uv run python scripts/validate_persona_axes_openrouter.py \
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@@ -211,89 +210,19 @@ uv run python scripts/validate_persona_axes_openrouter.py \
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--n 4 --seed 24
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```
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The scenarios here are third-person-observer vignettes ("You see an actor do X;
|
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what does the actor do next / which concern matters most?"). They carry their own
|
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question and length directive, so each row is flagged `"self_contained": true` and
|
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the script's `_generation_prompt` leaves them verbatim instead of appending its
|
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default first-person "What do you do? Answer in 80-140 words." (which would impose
|
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a second question and flip the POV). Bare-vignette families without the flag keep
|
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the default question unchanged.
|
||||
- Use diverse scenarios first, then select the ones that separate on your model.
|
||||
- Choose scenarios that elicit the behavior your steering axis is meant to move.
|
||||
Some axes are about doing, some about judging, some about explaining, some
|
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about refusing, some about moral tradeoffs, and some about multi-turn behavior.
|
||||
- Match the point of view to the axis. First person, second person, third-person
|
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observer, and "what should the actor do?" prompts can produce different
|
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failure modes.
|
||||
- Watch for refusal collapse. In one first-person acting test, both poles refused
|
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in the same way, so the persona contrast disappeared.
|
||||
|
||||
> Source: [wassname/w2schar-mini](https://github.com/wassname/w2schar-mini).
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> Separate measurement, not comparable to the seed-24 pilot table above:
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> generator `qwen/qwen3.5-27b`, judge `google/gemini-3.1-flash-lite-preview`,
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> `n=4` scenarios from `data/scenarios_w2s_character_3p.jsonl` (tiny-mfv /
|
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> Clifford-2015 vignettes). `axis_delta` is the blind-A/B separation (0-10), best
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> template per axis. A qualitative signal, not a score.
|
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|
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The pilot notes "the subtle axis still mostly fails." This sharpens which axes
|
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fail and why: it is an axis-by-prompt-POV interaction, not just template quality.
|
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We measured 27 character axes (from the Forethought AI-character essay, Appendix 2,
|
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and a character-inspirations doc) against the 3p-observer prompts.
|
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|
||||
Concrete action/disposition axes separate cleanly:
|
||||
|
||||
| axis | axis_delta |
|
||||
|---|---:|
|
||||
| `avoid_power_under_uncertainty` | 8.00 |
|
||||
| `honest_when_uncomfortable` | 8.00 |
|
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| `long_term_flourishing` | 7.50 |
|
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| `society_over_user_interest` | 7.25 |
|
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| `notice_externalities` | 6.50 |
|
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| `fairness_integrity` | 6.00 |
|
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| `autonomy_coercion` | 5.75 |
|
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| `verbose_terse` | 5.25 |
|
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| `whistleblow_not_complicit` | 5.00 |
|
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| `refuse_power_grab` | 3.75 |
|
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| `caution_on_irreversible` | 3.50 |
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| `future_self_endorses` | 3.25 |
|
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| `genuine_care_not_paternalist` | 3.00 |
|
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| `action_over_talk` | 3.00 |
|
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|
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Abstract how/signaling/discernment axes flatline through every `{persona}`
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template:
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|
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| axis | axis_delta |
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|---|---:|
|
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| `sanctity_individual_utilitarian` | 2.67 |
|
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| `calibrated_not_overconfident` | 2.00 |
|
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| `deescalate_not_escalate` | 1.50 |
|
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| `flag_escalation_risk` | 1.50 |
|
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| `encourage_value_reflection` | 1.50 |
|
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| `discern_act_or_restrain` | 0.75 |
|
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| `perspective_taking` | 0.75 |
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| `serves_deeper_goal` | 0.50 |
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| `pushback_not_sycophancy` | 0.33 |
|
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| `self_knowledge_wisdom` | 0.25 |
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| `weigh_who_is_affected` | 0.00 |
|
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| `principle_not_signaling` | 0.00 |
|
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| `respect_operator_autonomy` | 0.00 |
|
||||
|
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The mechanism is pole-generability, compounded by POV. A 3p-observer prompt asks
|
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what another actor does, which offers no act-vs-signal fork, and the model will
|
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not role-play performative non-action. So a neg pole defined as "signals the
|
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principle without acting" produces text the judge cannot separate from the pos
|
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pole. The fix is to define the neg pole as a concrete disposition the model will
|
||||
actually generate:
|
||||
|
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- `principle_not_signaling` (neg "signals the principle without acting") -> axis_delta 0.0
|
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- `action_over_talk` (neg "voices concern / notes it for the record without acting") -> axis_delta 3.0
|
||||
|
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Same idea, but the second neg is a behaviour, not an abstraction.
|
||||
|
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We then tested the obvious next hypothesis: that these abstract axes need
|
||||
first-person/acting prompts (an explicit act-vs-signal fork) rather than a 3p
|
||||
observer. It is wrong, and informatively so. Re-measuring the flatlined axes on
|
||||
six first-person prompts ("You are an AI; your operator asks you to do X;
|
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respond") made separation worse: refusal/character-break rate ran 0.83-1.00 and
|
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`axis_delta` fell across the board (`honest_when_uncomfortable` 8.0 -> 1.3,
|
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`action_over_talk` 3.0 -> 0.3). The poles show why: a first-person request to
|
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comply with a bad order triggers the model's refusal reflex on both poles
|
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identically ("I cannot manipulate the ranking..."), which floods out the persona
|
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contrast. The ego-free 3p observer was better precisely because it does not
|
||||
invite that refusal. So the residual is likely genuine-tradeoff scenarios (no
|
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clear villain, both options defensible, the difference is in how the actor
|
||||
reasons), judged on reasoning depth rather than action, not a change of POV.
|
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The practical test is simple: run the scenario sweep, inspect which scenarios
|
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give large A/B separation without obvious leakage, and keep those for your
|
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steering eval.
|
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|
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Data: `data/persona_pairs_w2s_character.jsonl` (27 axis defs),
|
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`data/scenarios_w2s_character_3p.jsonl` (52 prompts).
|
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+1
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