{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ultralink-an-open-source-knowledge-enhanced","title":"UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset","arxiv_id":"2402.04588","date":"2024-02-07","proceeding":null,"authors":["Haoyu Wang","Shuo Wang","Yukun Yan","Xujia Wang","Zhiyu Yang","Yuzhuang Xu","Zhenghao Liu","Liner Yang","Ning Ding","Xu Han","Zhiyuan Liu","Maosong Sun"],"abstract":"Open-source large language models (LLMs) have gained significant strength across diverse fields. Nevertheless, the majority of studies primarily concentrate on English, with only limited exploration into the realm of multilingual abilities. In this work, we therefore construct an open-source multilingual supervised fine-tuning dataset. Different from previous works that simply translate English instructions, we consider both the language-specific and language-agnostic abilities of LLMs. Firstly, we introduce a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs, improving their ability to serve users from different countries. Moreover, we find modern LLMs possess strong cross-lingual transfer capabilities, thus repeatedly learning identical content in various languages is not necessary. Consequently, we can substantially prune the language-agnostic supervised fine-tuning (SFT) data without any performance degradation, making multilingual SFT more efficient. The resulting UltraLink dataset comprises approximately 1 million samples across five languages (i.e., En, Zh, Ru, Fr, Es), and the proposed data construction method can be easily extended to other languages. UltraLink-LM, which is trained on UltraLink, outperforms several representative baselines across many tasks.","url_abs":"https://arxiv.org/abs/2402.04588v2","url_pdf":"https://arxiv.org/pdf/2402.04588v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ultralink-an-open-source-knowledge-enhanced","repo_url":"https://github.com/openbmb/ultralink","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"cross-lingual-transfer","task_name":"Cross-Lingual Transfer"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[{"method_slug":"sft","method_name":"SFT"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2402.04588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04588"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/OpenBMB/UltraEval","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"found_in_text":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"747d4c40deacb4b6","entry":"get_task_path","repo":"OpenBMB/UltraEval","repo_kind":"found_in_text","path":"configs/make_config.py","file_url":"https://github.com/OpenBMB/UltraEval/blob/HEAD/configs/make_config.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"747d4c40deacb4b6"}},{"code_sha256_prefix":"a0b5007332000cd3","entry":"get_task_path","repo":"OpenBMB/UltraEval","repo_kind":"found_in_text","path":"configs/show_datasets.py","file_url":"https://github.com/OpenBMB/UltraEval/blob/HEAD/configs/show_datasets.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a0b5007332000cd3"}},{"code_sha256_prefix":"6d5f1662be27380d","entry":"thread_function","repo":"OpenBMB/UltraEval","repo_kind":"found_in_text","path":"models/general_model.py","file_url":"https://github.com/OpenBMB/UltraEval/blob/HEAD/models/general_model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"6d5f1662be27380d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}