Update persona template library dataset

README.md

@@ -23,10 +23,14 @@ configs:
   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
@@ -41,7 +45,7 @@ How do we know if a persona template is good? We want on-axis variation, but not
 
 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. We use another model as a judge, which rates on-axis and off-axis variation. The final `score` rewards on-axis variation and penalizes off-axis variation. Style movement, persona echo, and refusals are kept as audit columns.
+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.
 
@@ -49,11 +53,13 @@ I am collecting reusable templates here, not large engineered suffix prompts. Th
 
 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`.
+Start with `main` for one row per reusable template.
 
 The main column is `score`, a conservative 0-100 clean-axis score:
 
@@ -61,7 +67,7 @@ The main column is `score`, a conservative 0-100 clean-axis score:
 score = 100 * on_axis * (1 - off_axis)
 ```
 
-`on_axis` is normalized from the intended-axis judge rating. `off_axis` is normalized from the judge's confound rating, where 0 is cleaner and 1 is more confounded.
+`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.
 
@@ -71,17 +77,29 @@ Low score can mean either no intended-axis movement or too much confounding. Rea
 
 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 measured template/persona-pair cell.
-2. `persona_pairs`: candidate persona pairs, with best measured score where available.
-3. `examples`: paired completions and judge ratings behind the score.
+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
 

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