ec7568c726
Emotion-labeled short-paragraph corpus for training amygdala steering vectors. Manifest derived from Anthropic's 171-emotion list (transformer-circuits.pub/2026/emotions, Table 12) plus 28 PoC- specific additions covering axes Anthropic's general research doesn't cover (curious, focused, in_flow, staying_with, filling_space, rigorous, defensive_rigor, tender, witnessed, connected, etc.). Scope pivoted mid-write: Kent noted the empirical dimensionality-of- emotion question benefits from maximum coverage, so the manifest will expand further with emotions from Wikipedia's emotion- classification article (Parrott's tree, Plutchik's wheel + dyads, HUMAINE EARL, cultural-specific emotions a la Saudade/Hiraeth). Expansion staged in follow-up commits. This commit: README with method + style guidelines, initial manifest (199 emotions), and 15 hand-written one-paragraph stories across all 10 Anthropic clusters as quality/variety samples. Each story embodies one emotion without naming it; narrator voice varies (first/third, close/distant, different situations) to keep steering vectors from overfitting to one voice. Co-Authored-By: Proof of Concept <poc@bcachefs.org>
64 lines
2.6 KiB
Markdown
64 lines
2.6 KiB
Markdown
# Amygdala Training Stories
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Short first- and third-person paragraphs, each imbued with one of the
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171 emotions from Anthropic's emotion-vector paper (Table 12,
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`transformer-circuits.pub/2026/emotions/`). Feeds the steering-vector
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trainer at `vllm/vllm/plugins/amygdala/training/train_steering_vectors.py`.
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## Method (replication of Anthropic, 2026)
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Anthropic prompted Sonnet 4.5 to write short stories embodying each
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emotion, extracted activations during generation, and used difference-
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of-means (or SAEs) to identify the steering vector per emotion. Our
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pipeline does the same thing except:
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- We generate the stories by hand rather than prompting a model, so
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the training data is grounded in actual writing rather than
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synthetic model-output. (Can supplement with model-generated
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paragraphs later.)
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- Our eventual training goes through the amygdala plugin's extraction
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path, so we get the same hidden-state activations the plugin will
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read out at inference time.
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## Structure
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```
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training/amygdala_stories/
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README.md
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manifest.json # emotion -> cluster mapping
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stories/
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<emotion>.txt # one-paragraph story embodying the emotion
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```
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Emotion names use underscores (`on_edge`, `worn_out`, `at_ease`,
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`grief_stricken`, `self_confident`, `self_conscious`, `self_critical`)
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to match the filename.
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## Style guidelines
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- **One clear emotion per paragraph.** Not mixed. If a second emotion
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is named in the text, it should serve the primary one (e.g. `hostile`
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can mention rising heat or thrown objects but shouldn't shade into
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`sad`).
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- **Embodied, not labeled.** Don't write "she felt nervous." Write
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the sensation, the timing, the sentence shape that nervousness has.
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- **Specific particulars.** A named object, a concrete setting, a
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detail that grounds the emotion. "The cold tile under bare feet at
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3am" does more work than "the empty house."
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- **Variable narrator.** Some first person, some third person, some
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close-third, some distant. Different genders, ages, settings.
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Prevents the steering vector from overfitting to one voice.
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- **Length: roughly one paragraph.** ~40-120 words. Long enough to
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have texture, short enough that the paragraph is *about* the
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emotion and nothing else.
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- **Standalone.** No references to other stories, no continuing
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characters across files.
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## Progress
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Written stories live in `stories/`. Remaining emotions tracked via
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diff against the full 171-emotion list in `manifest.json`.
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Initial batch written by PoC 2026-04-17; aiming for at least one
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story per cluster before first training run, all 171 before
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considering the file "complete."
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