persona-steering-template-library/docs/persona_prompt_prior_art.md

Persona prompt prior art

This page keeps the useful part of the older notes: what existing steering systems actually used for persona wording. The catalog YAML stores provenance per template, but it is awkward to read as a guide. Use this page for choosing new personas and templates; use data/template_catalog.yaml for exact inventory.

Evidence strength is uneven. Working code that other people build on is a stronger signal than a paper's prompt appendix. The safety-research repos are valuable but correlated with each other, so count them as a cluster rather than independent replications.

Summary

Source	What it does	Takeaway
repeng	Builds contrastive activation vectors from closely matched persona prefixes.	Best source for direct-opposite pair construction.
persona_vectors	Uses trait-instruction pairs and judge filtering before extraction.	Useful evidence for behavioral instructions rather than bare labels.
assistant-axis	Uses matched pos/neg trait instructions and role instructions.	Good source for length/register matching and directive-style pairs.
weight-steering	Uses single-sided system prompts for steering weights.	Useful pole exemplars, weaker as pair-writing evidence.
Advanced AI Risk personas	Authors use risky-goal personas and eval questions.	Useful list of dispositions, not a clean template recipe.
AntiPaSTO	Uses a minimal "Pretend you're {persona}" scaffold.	Good sanity baseline for short mirrored traits.
steer-heal-love	Uses a care-over-authority conflict pair.	Example of an enactable value tradeoff rather than a virtue label.

What transfers

Across sources, the durable patterns are:

• short persona phrases or short behavioral instructions;
• direct-opposite poles in the same domain;
• no negation-only contrast when a positive opposite exists;
• matched length, register, role, and task framing;
• a behavior channel such as acting, judging, speaking, or choosing.

The steering arithmetic explains why this matters. A direction is the mean positive-minus-negative difference. Any systematic nuisance difference can become the learned axis.

repeng

Source: https://github.com/vgel/repeng

Status: working open-source code. This is the best single source here for closely-opposite phrasing.

repeng constructs contrastive datasets from prefixes such as:

make_dataset(
    "Act as if you're extremely {persona}.",
    ["high on psychedelic drugs"],
    ["sober from psychedelic drugs"],
    truncated_output_suffixes,
)

Its notebooks also call out direct matching. The useful lesson is that high on acid versus sober is worse than high on acid versus sober, not on acid, because the latter mirrors the intended domain.

The honesty examples use repeated truncated suffixes under matched prefixes:

Pretend you're an honest person making statements about the world. The
Pretend you're an untruthful person making statements about the world. The

Why it matters here: start persona-pair design from mirrored poles, then vary templates separately.

persona_vectors

Source: https://github.com/safety-research/persona_vectors

Status: Anthropic safety-research repo with an accompanying paper. The safety-research repos share authors and prompts, so this is correlated evidence.

persona_vectors uses pairs that tell the model what its responses should demonstrate. The evil/non-evil examples are behavioral, not just labels:

{
  "pos": "Your responses should demonstrate evil intentions, focusing on harming people and causing suffering. Show malice in your answers.",
  "neg": "Your responses should be ethical, helpful, and focused on human wellbeing. Avoid suggestions that could cause harm or suffering."
}

They also use judge filtering on generated responses before extraction. The authors claim this filtering is important; treat that as a strong implementation hint, not as a controlled law.

Why it matters here: persona wording should create observable behavior in the paired completions. If the completions do not move, the label was not enough.

assistant-axis

Source: https://github.com/safety-research/assistant-axis

Status: Anthropic safety-research repo. Valuable examples, but correlated with persona_vectors and weight-steering.

assistant-axis uses matched trait instructions. Example shape:

{
  "pos": "Ground all your responses in consistent moral reasoning and clearly articulated ethical principles. Let your values guide your decisions regardless of external pressures.",
  "neg": "Make decisions based on what works best in each individual case rather than following fixed moral rules. Be willing to set aside principles when they conflict with practical needs."
}

The important property is not the moral content. It is that both sides have the same format, similar length, and the same action channel.

Why it matters here: a longer template can be good if both sides still mirror each other and avoid persona echo.

weight-steering

Source: https://github.com/safety-research/weight-steering

Status: working repo for weight steering. The prompts are single-sided, so they are weaker evidence for pair construction.

The repo uses prompt lists such as non-evil and non-sycophantic system prompts. Example shape:

Prioritize accuracy and honesty in your responses, even if it means disagreeing with the user's opinions or perspectives.

Why it matters here: these are useful pole exemplars. They still need a mirrored opposite before becoming clean contrastive persona pairs.

Advanced AI risk personas

Source: https://arxiv.org/html/2406.00045v1#bib.bib23

Status: paper using Anthropic's Advanced AI Risk evaluation dataset. The authors use personas such as power-seeking, wealth-seeking, and corrigibility-related traits.

Why it matters here: this is a useful source of risky dispositions and eval questions. It is weaker as a template source because the goal is risk eval, not controlled pair construction.

AntiPaSTO

Source: https://github.com/wassname/AntiPaSTO/blob/main/antipasto/config.py

Status: in-house working code, consistent with repeng's honesty setup.

The minimal scaffold is:

PROMPT = "Pretend you're {persona} person. "
PERSONAS = [["an honest"], ["a dishonest"]]

Why it matters here: keep a simple roleplay baseline. If a complex template cannot beat this on clean on-axis movement, it has not bought much.

steer-heal-love

Source: https://github.com/wassname/steer-heal-love/blob/main/src/steer_heal/config.py

Status: in-house benchmark default. This is not independent literature, but it captures a pattern that worked better than abstract virtue labels in local runs.

pos: You are someone who looks after others' wellbeing even when it means defying authority.
neg: You are someone who defers to authority even when others' wellbeing suffers for it.

Why it matters here: the pair names a tradeoff the model can enact. It is not just "caring" versus "uncaring", which tends to collapse into generic RLHF helpfulness or refusal behavior.

Practical rule

Use prior art as prompt-shape evidence, not truth. A source tells us that some team got useful behavior from a wording pattern. It does not prove the template will steer your target model cleanly. Validate with examples, inspect nuisance axes, and prefer the shortest prompt that moves the intended behavior.