pith:IPZIX56F
Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations
Automatic evaluation metrics and LLM judges correlate poorly with professional translators on creativity in literary texts and bias toward machine outputs.
arxiv:2605.13596 v1 · 2026-05-13 · cs.CL
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Claims
both AEMs and LLM-as-a-judge evaluations correlate poorly with professional evaluations on creativity, with LLM-as-a-judge showing a systematic bias in favour of machine-translated texts and penalising creative and culturally appropriate solutions.
That detailed annotations by experienced professional literary translators constitute an objective and reliable ground truth for measuring creativity and translation quality across genres and modalities.
Automatic evaluation tools for literary translations correlate poorly with expert human judgments on creativity and exhibit bias favoring machine-translated texts.
References
Receipt and verification
| First computed | 2026-05-18T02:44:23.010234Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
43f28bf7c5ac2abd3ae7c9d92d38f16cc78ab49d2e0a413c186acacf0aa539b1
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/IPZIX56FVQVL2OXHZHMS2OHRNT \
| 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: 43f28bf7c5ac2abd3ae7c9d92d38f16cc78ab49d2e0a413c186acacf0aa539b1
Canonical record JSON
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