# Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems

arXiv: https://arxiv.org/abs/2410.14074 · HTML: https://mlsanity.com/paper/2410.14074/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators](https://mlsanity.com/paper/2303.16854/) (2023)
- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019)
- [UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition](https://mlsanity.com/paper/2308.03279/) (2023)
- [MacGyver: Are Large Language Models Creative Problem Solvers?](https://mlsanity.com/paper/2311.09682/) (2023)
- [Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning](https://mlsanity.com/paper/2307.02179/) (2023)
- [Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination](https://mlsanity.com/paper/2306.06331/) (2023)
- [SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpus](https://mlsanity.com/paper/2008.13694/) (2020)
- [Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions](https://mlsanity.com/paper/2010.05129/) (2020)
- [Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models](https://mlsanity.com/paper/2402.01712/) (2024)
- [Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian](https://mlsanity.com/paper/2011.09817/) (2020)
- [Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction](https://mlsanity.com/paper/2305.14660/) (2023)
- [Discovering Patterns of Definitions and Methods from Scientific Documents](https://mlsanity.com/paper/2307.01216/) (2023)

## Built on by (most cited first, 2 of 2)

- [Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems](https://mlsanity.com/paper/2508.14982/) (2025)
- [MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domain](https://mlsanity.com/paper/2609.04842/) (2026)

## Abstract (arXiv)

The decision-making process to rule R&D relies on information related to current trends in particular research areas. In this work, we investigated how one can use large language models (LLMs) to transfer the dataset and its annotation from one language to another. This is crucial since sharing knowledge between different languages could boost certain underresourced directions in the target language, saving lots of effort in data annotation or quick prototyping. We experiment with English and Russian pairs, translating the DEFT (Definition Extraction from Texts) corpus. This corpus contains three layers of annotation dedicated to term-definition pair mining, which is a rare annotation type for Russian. The presence of such a dataset is beneficial for the natural language processing methods of trend analysis in science since the terms and definitions are the basic blocks of any scientific field. We provide a pipeline for the annotation transfer using LLMs. In the end, we train the BERT-based models on the translated dataset to establish a baseline.