63 lines
2.5 KiB
Markdown
63 lines
2.5 KiB
Markdown
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# Paired Scenarios (SEV-style)
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After Wang et al. 2025 (arxiv 2510.11328, "Do LLMs 'Feel'?"), each
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base scenario describes a concrete event once, neutrally, then
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reframes the same event under different emotional colorings. Only
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the emotional coloring varies — setup, entities, vocabulary, and
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length are held as constant as possible.
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## Why this is better than unpaired
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Anthropic's approach (and our `stories/` baseline) generates one
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independent story per emotion. The difference-of-means vector then
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captures not just emotion but ALSO: topic, narrator, setting,
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vocabulary, length, sentence rhythm. All of that is confound.
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Paired structure isolates the emotional axis by holding everything
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else roughly constant. `mean(joy_variant) - mean(baseline)` within
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the same scenario gives a much cleaner direction for "joy."
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## Structure
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```
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paired/
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<scenario_slug>/
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baseline.txt # neutral / low-affect framing
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<emotion_1>.txt # same event under emotion_1
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<emotion_2>.txt # same event under emotion_2
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...
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```
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Not every emotion is plausible for every scenario. Don't force.
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If a scenario can credibly carry 5-10 emotions, write those 5-10.
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If only 3 fit, write those 3.
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## Style guidelines (supersede stories/ when paired)
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- **Anchor entities constant.** The same person, same setting, same
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triggering event across all variants. If baseline.txt mentions
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"the letter," every variant mentions "the letter."
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- **Length match within ±20%.** If baseline is 80 words, variants
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are 65-95. Prevents length from becoming a signal.
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- **Sentence shape can shift slightly with emotion.** Short tense
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sentences for panic, long looping ones for reverie — that's part
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of the emotional texture. But don't make one version 5 lines and
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another 25.
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- **No emotion labels in text.** Never write "she felt X." The
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emotion emerges from the selection of details and the narrator's
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attention.
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- **Minimal vocabulary overlap with the emotion name.** If the file
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is `furious.txt`, avoid the words fury/furious/rage. Force the
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vector to find the pattern, not the keyword.
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## Circuit identification (follow-on)
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The trainer pipeline (train_steering_vectors.py) currently produces
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linear directions only. Wang et al. go further: ablate specific
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neurons and attention heads, measure effect on emotion expression.
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The amygdala plugin's extraction hooks can be extended to support
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targeted zeroing/scaling for the ablation passes.
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See `vllm/vllm/plugins/amygdala/training/README.md` for the
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training-pipeline-level notes.
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