pith:NJBT33RY
Prosocial Persuasion at Scale? Large Language Models Outperform Humans in Donation Appeals Across Levels of Personalization
LLM-generated donation appeals produced more donations, higher engagement, and stronger persuasiveness ratings than human-written ones in two experiments.
arxiv:2604.03202 v2 · 2026-04-03 · cs.CY
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Claims
In both experiments, LLM-generated content yielded more donations, resulted in higher engagement, and was rated as more persuasive than human-authored content.
That distributing a small bonus in an online experiment accurately measures real-world charitable donation behavior and that human and LLM content were produced under equivalent effort and quality constraints.
LLM-generated donation appeals outperform human-written ones in driving donations, engagement, and perceived persuasiveness across generic, personalized, and falsely personalized conditions.
Receipt and verification
| First computed | 2026-07-21T01:20:47.303067Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6a433dee38260652a1c7d18073287cc68a541937d95e094898471b5117bccd04
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· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/NJBT33RYEYDFFIOH2GAHGKD4Y2 \
| jq -c '.canonical_record' \
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# expect: 6a433dee38260652a1c7d18073287cc68a541937d95e094898471b5117bccd04
Canonical record JSON
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