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arXiv 2605.13919cs.CLcs.LG
多语言知识编辑中合并方法的融合：一项实证探索
Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
Department of Computer Science and Engineering, POSTECH（POSTECH计算机科学与工程系）；Designovel Co., Ltd.（Designovel公司）；LLSOLLU；Graduate School of Artificial Intelligence, POSTECH（POSTECH人工智能研究生院）
Multilingual knowledge editing (MKE) remains challenging because language-specific edits interfere with one another, even when locate-then-edit methods work well in monolingual settings. This paper focuses on three issues: the effectiveness of vector merging methods for MKE, the extent to which Task Singular Vectors for Merging (TSVM) can reduce multilingual interference, and the influence of the weight scaling factor and rank compression ratio on performance. We evaluate six merging variants with two popular backbone large language models, two base knowledge editing methods, and 12 languages on the MzsRE benchmark under a large-scale batch-editing setting. Our results show that vector summation with shared covariance is the most reliable overall strategy, whereas simple summation without shared covariance performs poorly. TSVM improves performance in some settings, but its ability to mitigate multilingual interference is limited. We also find that performance is sensitive to both weight scale and rank ratio, with larger-than-default scaling and relatively low rank often yielding better results. These findings clarify the practical strengths and limits of current vector merging methods for MKE and provide guidance for future multilingual knowledge editing research.
Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；National Institute of Standards and Technology（美国国家标准与技术研究院）；INSAIT（保加利亚索菲亚大学INSAIT研究院）；Sofia University “St. Kliment Ohridski”（圣基里曼·奥赫里德斯基索菲亚大学）
MEMLA: Enhancing Multilingual Knowledge Editing with Neuron-Masked Low-Rank Adaptation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Instituto de Telecomunicações（电信研究所）；Facebook AI（脸书人工智能研究院）；IQT Labs（IQT实验室）；University of Sheffield（谢菲尔德大学）；Imperial College London（伦敦帝国学院）；Unbabel（Unbabel公司）；Johns Hopkins University（约翰霍普金斯大学）；University of Wolverhampton（伍尔弗汉普顿大学）
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labels for up to 10,000 translations per language pair in the following formats: sentence-level direct assessments and post-editing effort, and word-level good/bad labels. It also contains the post-edited sentences, as well as titles of the articles where the sentences were extracted from, and the neural MT models used to translate the text.
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究所）
This article analyzes one month of edits to Wikipedia in order to examine the role of users editing multiple language editions (referred to as multilingual users). Such multilingual users may serve an important function in diffusing information across different language editions of the encyclopedia, and prior work has suggested this could reduce the level of self-focus bias in each edition. This study finds multilingual users are much more active than their single-edition (monolingual) counterparts. They are found in all language editions, but smaller-sized editions with fewer users have a hig…
Multilingualism is common offline, but we have a more limited understanding of the ways multilingualism is displayed online and the roles that multilinguals play in the spread of content between speakers of different languages. We take a computational approach to studying multilingualism using one of the largest user-generated content platforms, Wikipedia. We study multilingualism by collecting and analyzing a large dataset of the content written by multilingual editors of the English, German, and Spanish editions of Wikipedia. This dataset contains over two million paragraphs edited by over 1…
One Mind, Many Tongues: A Deep Dive into Language-Agnostic Knowledge Neurons in Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Concept-to-text Natural Language Generation is the task of expressing an input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the input. However, this often requires that the input appears verbatim in the output text. This poses challenges in multilingual settings, where the task expands to generate the output text in multiple languages given the same input. In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic De…
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
Stanford University（斯坦福大学）；Tianjin University（天津大学）；Microsoft（微软）；Université Paris-Saclay（巴黎萨克雷大学）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Hanyang University（汉阳大学）；Karya Inc.（Karya公司）
We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw, unstructured text. We release the full corpus as a resource to aid on…
This paper describes a new system for semi-automatically building, extending and managing a terminological thesaurus---a multilingual terminology dictionary enriched with relationships between the terms themselves to form a thesaurus. The system allows to radically enhance the workflow of current terminology expert groups, where most of the editing decisions still come from introspection. The presented system supplements the lexicographic process with natural language processing techniques, which are seamlessly integrated to the thesaurus editing environment. The system's methodology and the r…
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究所）
This article analyzes users who edit Wikipedia articles about Okinawa, Japan, in English and Japanese. It finds these users are among the most active and dedicated users in their primary languages, where they make many large, high-quality edits. However, when these users edit in their non-primary languages, they tend to make edits of a different type that are overall smaller in size and more often restricted to the narrow set of articles that exist in both languages. Design changes to motivate wider contributions from users in their non-primary languages and to encourage multilingual users to …
Economic issues related to the information processing techniques are very important. The development of such technologies is a major asset for developing countries like Cambodia and Laos, and emerging ones like Vietnam, Malaysia and Thailand. The MotAMot project aims to computerize an under-resourced language: Khmer, spoken mainly in Cambodia. The main goal of the project is the development of a multilingual lexical system targeted for Khmer. The macrostructure is a pivot one with each word sense of each language linked to a pivot axi. The microstructure comes from a simplification of the expl…
This paper presents a high-quality multilingual dataset for the documentation domain to advance research on localization of structured text. Unlike widely-used datasets for translation of plain text, we collect XML-structured parallel text segments from the online documentation for an enterprise software platform. These Web pages have been professionally translated from English into 16 languages and maintained by domain experts, and around 100,000 text segments are available for each language pair. We build and evaluate translation models for seven target languages from English, with several d…
Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
Center for Language and Cognition, University of Groningen（格罗宁根大学语言与认知中心）；Institute for Logic, Language and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）
Centre for Translation Studies（翻译研究中心）；University of Surrey（萨里大学）；RGCL（语言计算研究组）；University of Wolverhampton（伍尔弗汉普顿大学）
Although emotions are universal concepts, transferring the different shades of emotion from one language to another may not always be straightforward for human translators, let alone for machine translation systems. Moreover, the cognitive states are established by verbal explanations of experience which is shaped by both the verbal and cultural contexts. There are a number of verbal contexts where expression of emotions constitutes the pivotal component of the message. This is particularly true for User-Generated Content (UGC) which can be in the form of a review of a product or a service, a …
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；IQVIA
Quality Estimation (QE) is an important component of the machine translation workflow as it assesses the quality of the translated output without consulting reference translations. In this paper, we discuss our submission to the WMT 2021 QE Shared Task. We participate in Task 2 sentence-level sub-task that challenge participants to predict the HTER score for sentence-level post-editing effort. Our proposed system is an ensemble of multilingual BERT (mBERT)-based regression models, which are generated by fine-tuning on different input settings. It demonstrates comparable performance with respec…
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
Penn State University（宾夕法尼亚州立大学）；Trinity College Dublin（都柏林圣三一学院）；MIT Lincoln Lab（麻省理工学院林肯实验室）；Visa Research（Visa研究）；Kempelen Institute of Intelligent Technologies（凯普勒智能技术研究所）
Expanding FLORES+ Benchmark for more Low-Resource Settings: Portuguese-Emakhuwa Machine Translation Evaluation
Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC / LASI)（人工智能与计算机科学实验室（LIACC / LASI））；Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院）；Centro de Linguística da Universidade do Porto (CLUP)（波尔图大学语言学中心（CLUP））；Faculdade de Engenharia da Universidade Lúrio（卢里奥大学工程学院）；Faculdade de Letras da Universidade do Porto（波尔图大学文学院）
