IndoPref: A Multi-Domain Pairwise Preference Dataset for Indonesian

• URL: http://arxiv.org/abs/2507.22159v1
• Date: Tue, 29 Jul 2025 18:46:25 GMT
• Title: IndoPref: A Multi-Domain Pairwise Preference Dataset for Indonesian
• Authors: Vanessa Rebecca Wiyono, David Anugraha, Ayu Purwarianti, Genta Indra Winata,
• Abstract summary: IndoPref is the first fully human-authored and multi-domain Indonesian preference dataset.<n>All annotations are written in Indonesian and evaluated using Krippendorff's alpha, demonstrating strong inter-annotator agreement.
• Score: 11.564887118533766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over 200 million people speak Indonesian, yet the language remains significantly underrepresented in preference-based research for large language models (LLMs). Most existing multilingual datasets are derived from English translations, often resulting in content that lacks cultural and linguistic authenticity. To address this gap, we introduce IndoPref, the first fully human-authored and multi-domain Indonesian preference dataset specifically designed to evaluate the naturalness and quality of LLM-generated text. All annotations are natively written in Indonesian and evaluated using Krippendorff's alpha, demonstrating strong inter-annotator agreement. Additionally, we benchmark the dataset across multiple LLMs and assess the output quality of each model.

Related papers

• Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question Answering [73.73820209993515]
  We introduce KoLasSimpleQA, the first benchmark evaluating the multilingual factual ability of Large Language Models (LLMs)<n>Inspired by existing research, we created the question set with features such as single knowledge point coverage, absolute objectivity, unique answers, and temporal stability.<n>Results show significant performance differences between the two domains.
  arXiv  Detail & Related papers  (2025-05-22T12:27:02Z)
• Regional Tiny Stories: Using Small Models to Compare Language Learning and Tokenizer Performance [1.1784026260358966]
  We focus on Hindi, Marathi, and Bengali, evaluating SLMs for regional language processing and understanding linguistic complexity.<n>Our analysis shows that language-specific tokenizers outperform general-purpose ones for Indian languages.<n>These findings advance both the practical application of SLMs to underserved languages and our theoretical understanding of neural language development.
  arXiv  Detail & Related papers  (2025-04-07T10:33:14Z)
• COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing [1.3062731746155414]
  COMI-LINGUA is the largest manually annotated Hindi-English code-mixed dataset.<n>It comprises 125K+ high-quality instances across five core NLP tasks.<n>Each instance is annotated by three bilingual annotators, yielding over 376K expert annotations.
  arXiv  Detail & Related papers  (2025-03-27T16:36:39Z)
• BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages [93.92804151830744]
  We present BRIGHTER, a collection of multi-labeled, emotion-annotated datasets in 28 different languages.<n>We highlight the challenges related to the data collection and annotation processes.<n>We show that the BRIGHTER datasets represent a meaningful step towards addressing the gap in text-based emotion recognition.
  arXiv  Detail & Related papers  (2025-02-17T15:39:50Z)
• Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages [0.0]
  We introduce Bhinneka Korpus, a multilingual parallel corpus featuring five Indonesian local languages. Our goal is to enhance access and utilization of these resources, extending their reach within the country.
  arXiv  Detail & Related papers  (2024-04-01T09:24:06Z)
• SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages [44.017657230247934]
  We present textitSemRel, a new semantic relatedness dataset collection annotated by native speakers across 13 languages. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences.
  arXiv  Detail & Related papers  (2024-02-13T18:04:53Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Improving Domain-Specific Retrieval by NLI Fine-Tuning [64.79760042717822]
  This article investigates the fine-tuning potential of natural language inference (NLI) data to improve information retrieval and ranking. We employ both monolingual and multilingual sentence encoders fine-tuned by a supervised method utilizing contrastive loss and NLI data. Our results point to the fact that NLI fine-tuning increases the performance of the models in both tasks and both languages, with the potential to improve mono- and multilingual models.
  arXiv  Detail & Related papers  (2023-08-06T12:40:58Z)
• NusaCrowd: Open Source Initiative for Indonesian NLP Resources [104.5381571820792]
  NusaCrowd is a collaborative initiative to collect and unify existing resources for Indonesian languages. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.
  arXiv  Detail & Related papers  (2022-12-19T17:28:22Z)
• OCNLI: Original Chinese Natural Language Inference [21.540733910984006]
  We present the first large-scale NLI dataset (consisting of 56,000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI) Unlike recent attempts at extending NLI to other languages, our dataset does not rely on any automatic translation or non-expert annotation. We establish several baseline results on our dataset using state-of-the-art pre-trained models for Chinese, and find even the best performing models to be far outpaced by human performance.
  arXiv  Detail & Related papers  (2020-10-12T04:25:48Z)
• Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity [67.36239720463657]
  Multi-SimLex is a large-scale lexical resource and evaluation benchmark covering datasets for 12 diverse languages. Each language dataset is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs. Owing to the alignment of concepts across languages, we provide a suite of 66 cross-lingual semantic similarity datasets.
  arXiv  Detail & Related papers  (2020-03-10T17:17:01Z)

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.