ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer

• URL: http://arxiv.org/abs/2502.21228v2
• Date: Mon, 03 Mar 2025 09:11:46 GMT
• Title: ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer
• Authors: Omer Goldman, Uri Shaham, Dan Malkin, Sivan Eiger, Avinatan Hassidim, Yossi Matias, Joshua Maynez, Adi Mayrav Gilady, Jason Riesa, Shruti Rijhwani, Laura Rimell, Idan Szpektor, Reut Tsarfaty, Matan Eyal,
• Abstract summary: We present ECLeKTic, a multilingual closed-book QA (CBQA) dataset that Evaluates Cross-Lingual Knowledge Transfer.<n>We detected information with uneven coverage across languages by controlling for presence and absence of Wikipedia articles in 12 languages.<n>We show that SOTA models struggle to effectively share knowledge across, languages even if they can predict the answer well for queries in the same language the knowledge was acquired in.
• Score: 42.44703812325259
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To achieve equitable performance across languages, multilingual large language models (LLMs) must be able to abstract knowledge beyond the language in which it was acquired. However, the current literature lacks reliable ways to measure LLMs' capability of cross-lingual knowledge transfer. To that end, we present ECLeKTic, a multilingual closed-book QA (CBQA) dataset that Evaluates Cross-Lingual Knowledge Transfer in a simple, black-box manner. We detected information with uneven coverage across languages by controlling for presence and absence of Wikipedia articles in 12 languages. We generated knowledge-seeking questions in a source language, for which the answer appears in a relevant Wikipedia article and translated them to all other 11 languages, for which the respective Wikipedias lack equivalent articles. Assuming that Wikipedia reflects the prominent knowledge in the LLM's training data, to solve ECLeKTic's CBQA task the model is required to transfer knowledge between languages. Experimenting with 8 LLMs, we show that SOTA models struggle to effectively share knowledge across, languages even if they can predict the answer well for queries in the same language the knowledge was acquired in.

Related papers

• Language Models' Factuality Depends on the Language of Inquiry [36.466186024957075]
  We introduce a benchmark of 10,000 country-related facts across 13 languages.<n>We propose three novel metrics: Factual Recall Score, Knowledge Transferability Score, and Cross-Lingual Factual Knowledge Transferability Score.<n>Our results reveal fundamental weaknesses in today's state-of-the-art LMs.
  arXiv  Detail & Related papers  (2025-02-25T08:27:18Z)
• CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering [42.92810049636768]
  Large Language Models (LLMs) are pretrained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge.<n>We explore the Cross-Lingual Self-Aligning ability of Language Models (CALM) to align knowledge across languages.<n>We employ direct preference optimization (DPO) to align the model's knowledge across different languages.
  arXiv  Detail & Related papers  (2025-01-30T16:15:38Z)
• Cross-Lingual Multi-Hop Knowledge Editing [53.028586843468915]
  We propose the Cross-Lingual Multi-Hop Knowledge Editing paradigm, for measuring and analyzing the performance of various SoTA knowledge editing techniques in a cross-lingual setup. Specifically, we create a parallel cross-lingual benchmark, CROLIN-MQUAKE for measuring the knowledge editing capabilities. Following this, we propose a significantly improved system for cross-lingual multi-hop knowledge editing, CLEVER-CKE.
  arXiv  Detail & Related papers  (2024-07-14T17:18:16Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation [21.980770995466134]
  We introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling.
  arXiv  Detail & Related papers  (2024-02-18T07:24:34Z)
• Cross-Lingual Knowledge Editing in Large Language Models [73.12622532088564]
  Knowledge editing has been shown to adapt large language models to new knowledge without retraining from scratch. It is still unknown the effect of source language editing on a different target language. We first collect a large-scale cross-lingual synthetic dataset by translating ZsRE from English to Chinese.
  arXiv  Detail & Related papers  (2023-09-16T11:07:52Z)
• Adapters for Enhanced Modeling of Multilingual Knowledge and Text [54.02078328453149]
  Language models have been extended to multilingual language models (MLLMs) Knowledge graphs contain facts in an explicit triple format, which require careful curation and are only available in a few high-resource languages. We propose to enhance MLLMs with knowledge from multilingual knowledge graphs (MLKGs) so as to tackle language and knowledge graph tasks across many languages.
  arXiv  Detail & Related papers  (2022-10-24T21:33:42Z)
• X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models [103.75890012041366]
  Language models (LMs) have proven surprisingly successful at capturing factual knowledge. However, studies on LMs' factual representation ability have almost invariably been performed on English. We create a benchmark of cloze-style probes for 23 typologically diverse languages.
  arXiv  Detail & Related papers  (2020-10-13T05:29:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.