Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning

• URL: http://arxiv.org/abs/2511.13368v1
• Date: Mon, 17 Nov 2025 13:41:31 GMT
• Title: Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning
• Authors: Kajetan Dymkiewicz, Ivan Vulic, Helen Yannakoudakis, Eilam Shapira, Roi Reichart, Anna Korhonen,
• Abstract summary: Large language models (LLMs) perform strongly across tasks and languages.<n>How improvements in one task or language affect other tasks and languages and their combinations remains poorly understood.
• Score: 61.568735270400786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) perform strongly across tasks and languages, yet how improvements in one task or language affect other tasks and languages and their combinations remains poorly understood. We conduct a controlled PEFT/LoRA study across multiple open-weight LLM families and sizes, treating task and language as transfer axes while conditioning on model family and size; we fine-tune each model on a single task-language source and measure transfer as the percentage-point change versus its baseline score when evaluated on all other task-language target pairs. We decompose transfer into (i) Matched-Task (Cross-Language), (ii) Matched-Language (Cross-Task), and (iii) Cross-Task (Cross-Language) regimes. We uncover two consistent general patterns. First, a pronounced on-task vs. off-task asymmetry: Matched-Task (Cross-Language) transfer is reliably positive, whereas off-task transfer often incurs collateral degradation. Second, a stable donor-recipient structure across languages and tasks (hub donors vs. brittle recipients). We outline implications for risk-aware fine-tuning and model specialisation.

Related papers

• The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs [45.08958917457921]
  Large language models (LLMs) still struggle across tasks outside of high-resource languages.<n>In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce.
  arXiv  Detail & Related papers  (2025-05-23T20:28:31Z)
• AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for Multilingual Semantic Textual Relatedness [16.896143197472114]
  This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian languages. We propose using machine translation for data augmentation to address the low-resource challenge of limited training data. We achieve competitive results in the shared task: our system performs the best among all ranked teams in both subtask A (supervised learning) and subtask C (cross-lingual transfer)
  arXiv  Detail & Related papers  (2024-04-01T21:21:15Z)
• AdaMergeX: Cross-Lingual Transfer with Large Language Models via Adaptive Adapter Merging [96.39773974044041]
  Cross-lingual transfer is an effective alternative to the direct fine-tuning on target tasks in specific languages. We propose a new cross-lingual transfer method called $textttAdaMergeX$ that utilizes adaptive adapter merging. Our empirical results demonstrate that our approach yields new and effective cross-lingual transfer, outperforming existing methods across all settings.
  arXiv  Detail & Related papers  (2024-02-29T07:11:24Z)
• CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval [5.97515243922116]
  We present the Charles University system for the MRL2023 Shared Task on Multi-lingual Multi-task Information Retrieval. The goal of the shared task was to develop systems for named entity recognition and question answering in several under-represented languages. Our solutions to both subtasks rely on the translate-test approach.
  arXiv  Detail & Related papers  (2023-10-25T10:22:49Z)
• Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog [67.20796950016735]
  Multi2WOZ dataset spans four typologically diverse languages: Chinese, German, Arabic, and Russian. We introduce a new framework for multilingual conversational specialization of pretrained language models (PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream TOD tasks. Our experiments show that, in most setups, the best performance entails the combination of (I) conversational specialization in the target language and (ii) few-shot transfer for the concrete TOD task.
  arXiv  Detail & Related papers  (2022-05-20T18:35:38Z)
• Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation [80.16548523140025]
  We extend the vanilla pretrain-finetune pipeline with extra code-switching restore task to bridge the gap between the pretrain and finetune stages. Our approach could narrow the cross-lingual sentence representation distance and improve low-frequency word translation with trivial computational cost.
  arXiv  Detail & Related papers  (2022-04-16T16:08:38Z)
• Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning [61.29879000628815]
  We show that it is crucial for tasks to align gradients between them in order to maximize knowledge transfer. We propose a simple yet effective method that can efficiently align gradients between tasks. We extensively validate our method on various multi-task learning and zero-shot cross-lingual transfer tasks.
  arXiv  Detail & Related papers  (2021-10-06T09:10:10Z)
• MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers [1.869621561196521]
  We present our approach for solving the SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC) The goal is to detect whether a given word common to both the sentences evokes the same meaning. We submit systems for both the settings - Multilingual and Cross-Lingual.
  arXiv  Detail & Related papers  (2021-04-04T08:49:28Z)
• From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers [62.637055980148816]
  Massively multilingual transformers pretrained with language modeling objectives have become a de facto default transfer paradigm for NLP. We show that cross-lingual transfer via massively multilingual transformers is substantially less effective in resource-lean scenarios and for distant languages.
  arXiv  Detail & Related papers  (2020-05-01T22:04:58Z)

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.