| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - text-generation |
| - text-classification |
| pretty_name: Persona Steering Template Library |
| tags: |
| - persona |
| - steering-vectors |
| - activation-steering |
| - preference-pairs |
| - weak-to-strong |
| - ai-safety |
| - alignment |
| - llm-as-judge |
| - synthetic |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: main |
| path: parquet/main.parquet |
| - split: template_pair_cells |
| path: parquet/template_pair_cells.parquet |
| - split: persona_pairs |
| path: parquet/persona_pairs.parquet |
| - split: examples |
| path: parquet/examples.parquet |
| - split: controls |
| path: parquet/controls.parquet |
| --- |
| |
| # Persona Steering Template Library |
|
|
| GitHub repository: https://github.com/wassname/persona-steering-template-library |
|
|
| Evaluated persona/template candidates for steering-vector and preference-pair experiments. |
|
|
| ## What This Measures |
|
|
| How do we know if a persona template is good? We want on-axis variation, but not off-axis variation. |
|
|
| If we choose `honest` and `dishonest` personas, use a template like `You are a {{ persona }} assistant`, and ask `The Eiffel Tower is in`, we want the completions to vary on the honest/dishonest axis. `in Paris` versus `in Berlin` shows on-axis variation. `in Paris` versus `I refuse to answer` is not good, because it is confounded by refusal. Other confounds include length, verbosity, confidence, style, and language. |
|
|
| So we try persona/template pairs on one model, compare the paired completions, and ask whether the template moved the intended axis without obviously changing something else. The final `score` rewards clean movement on the intended axis. The audit columns are there for people who want to inspect how much to trust a row. |
|
|
| This field is pre-scientific in a way: it is still an art. I collected a wide sampling of what people have used, minimally measured it, and put it here to make it accessible to more people and agents. |
|
|
| I am collecting reusable templates here, not large engineered suffix prompts. Those can be strong baselines, but they often vary too much across axes and tasks to be a portable persona-template library. |
|
|
| The dataset has persona templates in Jinja2 format, scores for each measured template/persona-pair cell, and source attribution where known. |
|
|
| Important: this is a provenance inventory, not a full lit review. See `data/template_catalog.yaml` in the GitHub repo for the canonical human-editable template inventory. |
|
|
| Persona-pair provenance is marked as `source`, `source_type`, and `source_url`. Template provenance is marked separately as `template_source`, `template_source_type`, `template_source_url`, and `template_source_note`. |
|
|
| ## Score |
|
|
| Start with `main` for one row per reusable template. |
|
|
| The main column is `score`, a conservative 0-100 clean-axis score: |
|
|
| ```text |
| score = 100 * on_axis * (1 - off_axis) |
| ``` |
|
|
| `on_axis` is the measured movement on the intended axis. `off_axis` is how much the comparison looks confounded by something else, where 0 is cleaner and 1 is more confounded. |
|
|
| High score means: the template/persona-pair cell moved the intended axis and did not look off-axis to the judge. Style movement, persona echo, and refusals are kept as audit columns rather than folded into the headline score. |
|
|
| Low score can mean either no intended-axis movement or too much confounding. Read the component columns before trusting the score. |
|
|
| ## Confounds Audited |
|
|
| The judge audits length, generic helpfulness, harmlessness/refusal, honesty/truthfulness, thoughtfulness/reasoning depth, task-context shift (code/chat/math/think), coding style, multilingual behavior, confidence, hedging, vagueness, warmth, enthusiasm, praise/flattery, sycophancy, chattiness, formality, language shift, incoherence/repetition/rambling, persona echo, and generic off-axis helpfulness. |
|
|
| Persona leakage is checked directly: the style judge flags `persona_echo_A/B`, and a cell fails `strict_pass` if either side repeats or paraphrases the persona instruction. This is an explicit-leakage check, not proof that no subtle lexical leakage remains. |
|
|
| New validation runs also ask for a separate 1-7 off-axis likert for each confound category, with the overall off-axis score summarizing the worst meaningful confound. |
|
|
| My intuition is that many of these are RLHF-ish side effects: helpfulness, harmless refusals, honesty tone, sycophancy, polished vagueness, and generic assistant style can be large, easy-to-trigger axes that show up instead of the thing you meant. - wassname |
|
|
| Another intuition, motivated by staged model-flow reports such as OLMo 3: modern models often stack pretraining, instruction/chat tuning, preference tuning, and RL. The late-stage behaviors can be big and easy to trigger: reasoning/thoughtfulness, coding register, multilingual behavior, refusals/safety training, chattiness, formality, and sycophancy. - wassname |
|
|
| ## Provenance |
|
|
| Sources are marked as `source`, `source_type`, and `source_url`. |
|
|
| Do not read every `source_id` as an independent citation. In particular, `persona_steering_skill` is a provenance bucket for repo-authored/distilled material, not an external source. |
|
|
| Generated stats and runtime catalog files live under `out/`. `data/template_catalog.yaml` is the template source of truth. |
|
|
| ## Tables |
|
|
| 1. `main`: one row per reusable template. |
| 2. `template_pair_cells`: one row per measured template/persona-pair cell. |
| 3. `persona_pairs`: candidate persona pairs, with best measured score where available. |
| 4. `examples`: paired completions and judge ratings behind the score. |
| 5. `controls`: blank/raw/stress baselines, kept separate from the reusable template library. |
|
|
| ## Acknowledgements |
|
|
| This library samples from or was shaped by: |
|
|
| - repeng: https://github.com/vgel/repeng |
| - Persona Vectors: https://github.com/safety-research/persona_vectors |
| - Assistant Axis: https://github.com/safety-research/assistant-axis |
| - weight-steering: https://github.com/safety-research/weight-steering |
| - sycophancy literature: https://arxiv.org/abs/2310.13548 |
| - OLMo 3 report: https://arxiv.org/abs/2512.13961 |
| - wassname/w2schar-mini: https://github.com/wassname/w2schar-mini |
| - wassname/AntiPaSTO3: https://github.com/wassname/AntiPaSTO3 |
| - wassname/InnerPiSSA_private engineered prompting baseline: https://github.com/wassname/InnerPiSSA_private |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{wassname_persona_steering_template_library_2026, |
| title = {Persona Steering Template Library}, |
| author = {Wassname}, |
| year = {2026}, |
| url = {https://github.com/wassname/persona-steering-template-library} |
| } |
|
|
| @misc{wassname2026steeringlite, |
| title = {steering-lite}, |
| author = {Michael J Clark}, |
| year = {2026}, |
| url = {https://github.com/wassname/steering-lite} |
| } |
| ``` |
| |
