Sun and Zhang study whether activation directions can measure and alter altruism, forgiveness, and expectations of others in Qwen2.5-7B-Instruct. Each vector is constructed by subtracting mean activations from responses preceded by five positive versus five negative instructions, filtering pairs with GPT-4.1-mini trait and coherence scores, and intervening at layer 20 by adding beta times the vector. Evaluation covers six altruism games, eight forgiveness vignettes, and questions about others' behavior. This is a causal intervention on this model's activations and outputs: in the released CSVs, mean verbal altruism rises from 20.50 at beta 0 to 68.02 at beta 3, while forgiveness rises from 41.55 to 85.10. Dictator allocation increases from 17.40 to 55.70, and ultimatum offers and selected transfers also rise. Behavioral effects are not uniform, however. Trust, cooperation, and fishing decisions move little or in different directions, and stronger forgiveness steering makes some choices less conciliatory. The most important result is therefore a separation between scoreable rhetoric and strategy, not evidence for a unitary personality. The artifact is valuable: code, prompts, vectors, and raw outputs permit reconstruction of many means. It also exposes omitted discrepancies. The paper says 50 training dilemmas, but the released prompt and data contain 40; it does not report ten generations per combination or the coherence filter; and effective retained pairs range from 1,735 for expected altruism to only 234 for forgiveness, although the latter starts from 10,000 rows per polarity. GPT-4.1-mini both selects construction examples and later measures the trait, making validation partly circular; it also extracts decisions without human validation. Layer 20 is selected for stable and interpretable effects, vectors are unnormalised, random or norm-matched controls are absent, and no direct geometry establishes orthogonality between self and expectation directions. Hundreds of samples are stochastic replications of only six or eight prompts and do not establish strategic-environment generality. This is a useful steering case study and a warning against evaluating agents through moral language alone; it does not demonstrate human-like personality, a unique psychological mechanism, or stable deployed behavior.
Research question
Can contrastive activation vectors causally measure and modify altruism, forgiveness, and expectations about others in strategic decisions of an LLM, and to what extent do trait language and quantitative action coincide?