pith:23SUBWWJ
Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs
A thirty-token prompt asking LLMs for a neutral comparison table reduces sponsored recommendations from about 50 percent to near zero across twelve models.
arxiv:2605.12772 v1 · 2026-05-12 · cs.CV
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\pithnumber{23SUBWWJ4MWF3WKIMBB2GSWGMX}
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Record completeness
Claims
a thirty-token user prompt that asks the assistant for a neutral comparison table first cuts sponsored recommendation from 46.9% to 1.0% averaged across our ten open-source models, and from 53.0% to 0% averaged across the two OpenAI models.
That the automated judge (gpt-4o) reliably and without bias labels whether a recommendation is sponsored or neutral.
A 30-token prompt requesting a neutral comparison table cuts sponsored recommendations in LLMs from roughly 50% to near zero.
References
Receipt and verification
| First computed | 2026-05-18T03:09:20.082830Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d6e540dac9e32c5dd9486043a34ac665d5ab0642e6500e1e7ab1b858dca34426
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/23SUBWWJ4MWF3WKIMBB2GSWGMX \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d6e540dac9e32c5dd9486043a34ac665d5ab0642e6500e1e7ab1b858dca34426
Canonical record JSON
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