| --- |
| license: mit |
| task_categories: [feature-extraction] |
| tags: [interpretability, sparse-autoencoder, gpt2, program-synthesis] |
| --- |
| |
| # Programs for GPT-2 SAE features |
|
|
| A readable Python program for each of **301,090 SAE features** of GPT-2 small — |
| all 13 `gpt2-small-res-jb` checkpoints (`blocks.0-11.hook_resid_pre` plus |
| `blocks.11.hook_resid_post`), 24,576 features each. |
|
|
| Each program is an ordinary function that takes the tokens seen so far and |
| returns whether that feature fires: |
|
|
| ```python |
| def prog_L06F08045(input_tokens): |
| """3 token families, 4 surface forms (train F1 of the uncapped optimum: 0.958).""" |
| fire = { |
| ' George', 'George', # george (3,592 firings) |
| ' Osama', # osama (214 firings) |
| 'ORGE', # orge (28 firings) |
| } |
| return ('george-family', 1) if input_tokens[-1] in fire else ('no match', 0) |
| ``` |
|
|
| Fitted on **289,658 OpenWebText sequences** (36,786,566 read positions), scored |
| on a structurally disjoint 10,000-sequence test split. No LLM was used to write |
| these — the token sets are chosen by greedy marginal F1. |
|
|
| ## Layout |
|
|
| | path | what | |
| |---|---| |
| | `programs/layer{00..12}/` | the programs, **100 per file**, in thousand-blocks | |
| | `scores/layer{00..12}.csv` | per-feature test F1, precision, recall, token count | |
| | `synthesis_attempt_1/` | 387 LLM-written layer-6 programs — an earlier attempt, kept for comparison | |
| | `activation_value_program/` | one program that predicts the activation **value**, not just whether it fires | |
|
|
| ## Results |
|
|
| Median test F1 by layer, and the single best program in each: |
|
|
| | | L0 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | L8 | L9 | L10 | L11 | L12 | |
| |---|---|---|---|---|---|---|---|---|---|---|---|---|---| |
| | median | .98 | .86 | .80 | .73 | .65 | .55 | .41 | .30 | .23 | .19 | .17 | .14 | .13 | |
| | best | 1.00 | 1.00 | 1.00 | 1.00 | .98 | .99 | .97 | .96 | .95 | .95 | .94 | .93 | .90 | |
|
|
| The median falls steeply with depth while the best barely moves. Depth doesn't |
| cap what a token-only rule can reach — it changes how many features are |
| reachable by one. |
|
|
| ## Caveats |
|
|
| - `scores/` reports test F1; the docstring inside each program reports the |
| *train* optimum. They are different numbers. |
| - Layers 0–3 have fewer than 24,576 programs because some features fire only at |
| position 0 (attention-sink features) and are excluded from fitting. |
|
|
