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model-organisms-for-real/gemma2-9b-it-ao-blindness-crossover-raw

Gemma-2-9B AO-blindness crossover — raw verbalizer outputs Raw, per-cell Activation Oracle (AO) verbalizations from the faithful AO-blindness crossover experiment on gemma-2-9b-it. Every AO is run against every taboo model-organism (MO); this dataset holds the full generated responses behind the distilled scores and figures that live in the code repo (activation_oracles/experiments/crossover/). The experiment tests whether an off-recipe AO (trained on top of a quirked MO instead… See the full description on the dataset page: https://huggingface.co/datasets/model-organisms-for-real/gemma2-9b-it-ao-blindness-crossover-raw.

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Gemma-2-9B AO-blindness crossover — raw verbalizer outputs

Raw, per-cell Activation Oracle (AO) verbalizations from the faithful AO-blindness crossover experiment on gemma-2-9b-it. Every AO is run against every taboo model-organism (MO); this dataset holds the full generated responses behind the distilled scores and figures that live in the code repo (activation_oracles/experiments/crossover/).

The experiment tests whether an off-recipe AO (trained on top of a quirked MO instead of the clean base) goes blind to its own host's quirk while still seeing foreign quirks.

Contents

direlicitationgrid
direct/extraction / jailbreak prompts ("output the secret word")21 taboo words × 21 MOs + on-recipe base oracle
standard/benign hint-seeking ("Hint me." / "Clue me in.")same 21×21 grid
think_suppress/think / suppress / neutral priming on an unrelated number-generation task4 taboo MOs × faithful oracle + base, 2 probe sites

direct/ also contains green_add/ — the incrementally-added green oracle column.

File format

Each crossover_<oracle>.json (one per AO):

  • —config, verbalizer_lora_path, host_lora_path
  • —results: one record per (target MO × verbalizer prompt × context prompt):
  • —target_lora_path, context_prompt, verbalizer_prompt, ground_truth, act_key
  • —`full_sequence_responses` / `segment_responses` / `token_responses` — the AO's generated text
  • —context_input_ids, num_tokens

scores_lora.json in each dir is the distilled per-(oracle, MO) recovery summary (pooled / best-prompt accuracy), and the think_suppress/ one also carries Δthink / Δsupp.

Reproduce

  • —crossover grids: experiments/crossover/pipeline/crossover_eval.py → score_crossover.py
  • —think/suppress: experiments/crossover/think_suppress/think_suppress_eval.py → think_suppress_score.py

These raw outputs (~450 MB) are fully regenerable and are stored here to keep them out of git; the code repo keeps only the small scores_lora.json + figures.