Pith Number

pith:4R7PKKQJ

pith:2026:4R7PKKQJMQIHUK5DI5DZY2JCJJ

not attested not anchored not stored refs resolved

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation

Anna Korhonen, Isabelle Augenstein, Lucas Resck

Consensus-driven preference optimization raises cross-language cultural consistency in multilingual LLMs by up to 0.10 points on a new metric.

arxiv:2605.12515 v1 · 2026-04-02 · cs.CL

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\pithnumber{4R7PKKQJMQIHUK5DI5DZY2JCJJ}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

C-3PO achieves up to a 0.10-point absolute increase in κ_S over unaligned models, outperforming strong prompting and representation steering baselines.

C2weakest assumption

That the consensus across languages in C-3PO represents genuine cultural consistency rather than an average that erases valid cultural differences, and that κ_S accurately isolates inconsistency without confounding factors.

C3one line summary

Multilingual LLMs display cross-lingual cultural inconsistency that a new metric quantifies and a consensus-driven preference optimization method reduces by up to 0.10 points.

References

32 extracted · 32 resolved · 3 Pith anchors

[1] Aligning LLM s for Multilingual Consistency in Enterprise Applications 2025 · doi:10.18653/v1/2025.emnlp-industry.9

[2] Mengyu Bu, Shaolei Zhang, Zhongjun He, Hua Wu, and Yang Feng. 2025. https://doi.org/10.18653/v1/2025.emnlp-main.328 AlignX : Advancing Multilingual Large Language Models with Multilingual Representati 2025 · doi:10.18653/v1/2025.emnlp-main.328

[3] doi: 10.1162/COLI.a.583 2025 · doi:10.1162/coli.a.583

[4] Menglong Cui, Pengzhi Gao, Wei Liu, Jian Luan, and Bin Wang. 2025. https://doi.org/10.18653/v1/2025.naacl-long.280 Multilingual Machine Translation with Open Large Language Models at Practical Scale : 2025 · doi:10.18653/v1/2025.naacl-long.280

[5] Constanza Fierro and Anders Søgaard. 2022. https://doi.org/10.18653/v1/2022.findings-acl.240 Factual Consistency of Multilingual Pretrained Language Models . In Findings of the Association for Computa 2022 · doi:10.18653/v1/2022.findings-acl.240

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:10:02.917458Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e47ef52a0964107a2ba347479c69224a774fea94bd759a4be9529b5d4209b380

Aliases

arxiv: 2605.12515 · arxiv_version: 2605.12515v1 · doi: 10.48550/arxiv.2605.12515 · pith_short_12: 4R7PKKQJMQIH · pith_short_16: 4R7PKKQJMQIHUK5D · pith_short_8: 4R7PKKQJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4R7PKKQJMQIHUK5DI5DZY2JCJJ \
  | 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: e47ef52a0964107a2ba347479c69224a774fea94bd759a4be9529b5d4209b380

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "925d3356ab40bdd208c2c4436f3bcf640b413cbf4c2603c4068da0dbed78185b",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-04-02T14:04:06Z",
    "title_canon_sha256": "25fcf3c30d3e00395363c679bb258c19c7ff50e91d34601619f56f9f27500b93"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12515",
    "kind": "arxiv",
    "version": 1
  }
}