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This paper empirically measures how ten linguistic features in fine-tuning data shift Llama-3.2-1B's reasoning on animal welfare, finding that assertive and moral language strengthens pro-animal-welfare stances while hedged and descriptive language dilutes them.
This paper examines when and why self-reported psychometric measures predict the actual behavior of large language models, finding that fine-grained, behavior-specific instruments (Theory of Planned Behavior) achieve human-level coherence within a shared conversation, while broad traits like Big 5 do not.
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