@inproceedings{rawat-etal-2026-indicripple,
title = "{I}ndic{R}ipple: Measuring Cross-Lingual Ripple Effects of Knowledge Editing in {I}ndic Languages",
author = "Rawat, Gunjan and
Venkatesh, Sneha and
Javaji, Shashidhar Reddy and
Zhu, Zining",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.171/",
pages = "3801--3819",
ISBN = "979-8-89176-439-2",
abstract = "Large language models can be updated after training by modifying parameters, conditioning on edit demonstrations, or supplying retrieved evidence. Yet successful factual updates should do more than change one prompt: they should generalize to related questions, transfer across languages, suppress obsolete facts, and preserve unrelated knowledge. This is the problem of measuring cross-lingual ripple effects of factual updates. The existing evaluations on multilingual knowledge injection, however, focus on high-resource languages like English, and it is unknown whether the findings can transfer to Indic languages. In this paper, we present IndicRipple, a controlled evaluation of multilingual factual updating over 500 factual updates across five domains and five languages: English, Hindi, Marathi, Telugu, and Kannada. We compare parametric editing, in-context editing, and retrieval-based correction using ripple-oriented probes for logical generalization, compositionality, subject aliasing, relation specificity, and forgetfulness. Our results show that current factual update mechanisms show limited cross-lingual ripple effects. IKE performs best overall but reaches only 21.07{\%} strict semantic-plus-language accuracy; RAG often recovers the edited fact but fails to answer in the requested language; and ROME/MEMIT are frequently language-compliant without semantically propagating the update. Strict accuracy is consistently higher for English evaluation than for the Indic-language evaluations. These findings show that direct update success overstates progress: multilingual factual updating must be evaluated as stable cross-lingual propagation, not isolated prompt-level overwrite."
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<abstract>Large language models can be updated after training by modifying parameters, conditioning on edit demonstrations, or supplying retrieved evidence. Yet successful factual updates should do more than change one prompt: they should generalize to related questions, transfer across languages, suppress obsolete facts, and preserve unrelated knowledge. This is the problem of measuring cross-lingual ripple effects of factual updates. The existing evaluations on multilingual knowledge injection, however, focus on high-resource languages like English, and it is unknown whether the findings can transfer to Indic languages. In this paper, we present IndicRipple, a controlled evaluation of multilingual factual updating over 500 factual updates across five domains and five languages: English, Hindi, Marathi, Telugu, and Kannada. We compare parametric editing, in-context editing, and retrieval-based correction using ripple-oriented probes for logical generalization, compositionality, subject aliasing, relation specificity, and forgetfulness. Our results show that current factual update mechanisms show limited cross-lingual ripple effects. IKE performs best overall but reaches only 21.07% strict semantic-plus-language accuracy; RAG often recovers the edited fact but fails to answer in the requested language; and ROME/MEMIT are frequently language-compliant without semantically propagating the update. Strict accuracy is consistently higher for English evaluation than for the Indic-language evaluations. These findings show that direct update success overstates progress: multilingual factual updating must be evaluated as stable cross-lingual propagation, not isolated prompt-level overwrite.</abstract>
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%0 Conference Proceedings
%T IndicRipple: Measuring Cross-Lingual Ripple Effects of Knowledge Editing in Indic Languages
%A Rawat, Gunjan
%A Venkatesh, Sneha
%A Javaji, Shashidhar Reddy
%A Zhu, Zining
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F rawat-etal-2026-indicripple
%X Large language models can be updated after training by modifying parameters, conditioning on edit demonstrations, or supplying retrieved evidence. Yet successful factual updates should do more than change one prompt: they should generalize to related questions, transfer across languages, suppress obsolete facts, and preserve unrelated knowledge. This is the problem of measuring cross-lingual ripple effects of factual updates. The existing evaluations on multilingual knowledge injection, however, focus on high-resource languages like English, and it is unknown whether the findings can transfer to Indic languages. In this paper, we present IndicRipple, a controlled evaluation of multilingual factual updating over 500 factual updates across five domains and five languages: English, Hindi, Marathi, Telugu, and Kannada. We compare parametric editing, in-context editing, and retrieval-based correction using ripple-oriented probes for logical generalization, compositionality, subject aliasing, relation specificity, and forgetfulness. Our results show that current factual update mechanisms show limited cross-lingual ripple effects. IKE performs best overall but reaches only 21.07% strict semantic-plus-language accuracy; RAG often recovers the edited fact but fails to answer in the requested language; and ROME/MEMIT are frequently language-compliant without semantically propagating the update. Strict accuracy is consistently higher for English evaluation than for the Indic-language evaluations. These findings show that direct update success overstates progress: multilingual factual updating must be evaluated as stable cross-lingual propagation, not isolated prompt-level overwrite.
%U https://aclanthology.org/2026.aacl-long.171/
%P 3801-3819
Markdown (Informal)
[IndicRipple: Measuring Cross-Lingual Ripple Effects of Knowledge Editing in Indic Languages](https://aclanthology.org/2026.aacl-long.171/) (Rawat et al., AACL-IJCNLP 2026)
ACL
- Gunjan Rawat, Sneha Venkatesh, Shashidhar Reddy Javaji, and Zining Zhu. 2026. IndicRipple: Measuring Cross-Lingual Ripple Effects of Knowledge Editing in Indic Languages. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3801–3819, Hengqin, China. Association for Computational Linguistics.