Demo Data from EDBT 2026 / reviewers in the wild / expert
Nurul Aisyah
dblp:346/6826
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Language models and text generation · 77% Knowledge representation and reasoning · 23% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computing education · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › evaluation of language models › multilingual evaluation
multilingual language model evaluation |
0.7 | 1 | 2023 | Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU · EMNLP 2023 |
Computing education
educational assessment |
0.7 | 1 | 2023 | Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU · EMNLP 2023 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge management › knowledge evaluation
cultural knowledge evaluation |
0.2 | 1 | 2023 | Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU · EMNLP 2023 |
Methods — techniques the papers use, named apart from their topics
benchmark construction · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Evaluating Vision-Language and Large Language Models for Automated Student Assessment in Indonesian Classrooms
Nurul Aisyah, Muhammad Dehan Al Kautsar, Arif Hidayat, Raqib Chowdhury, Fajri Koto |
AIED | 1 |
| 2024 | IndoCulture: Exploring Geographically Influenced Cultural Commonsense Reasoning Across Eleven Indonesian ProvincesabstractAbstract Although commonsense reasoning is greatly shaped by cultural and geographical factors, previous studies have predominantly centered on cultures grounded in the English language, potentially resulting in an Anglocentric bias. In this paper, we introduce IndoCulture, aimed at understanding the influence of geographical factors on language model reasoning ability, with a specific emphasis on the diverse cultures found within eleven Indonesian provinces. In contrast to prior work that has relied on templates (Yin et al., 2022) and online scrapping (Fung et al., 2024), we create IndoCulture by asking local people to manually develop a cultural context and plausible options, across a set of predefined topics. Evaluation of 27 language models reveals several insights: (1) the open-weight Llama–3 is competitive with GPT–4, while other open-weight models struggle, with accuracies below 50%; (2) there is a general pattern of models generally performing better for some provinces, such as Bali and West Java, and less well for others; and (3) the inclusion of location context enhances performance, especially for larger models like GPT–4, emphasizing the significance of geographical context in commonsense reasoning.1 Fajri Koto, Rahmad Mahendra, Nurul Aisyah, Timothy Baldwin |
Trans. Assoc. Comput. Linguistics | 3 |
| 2023 | Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLUabstractAlthough large language models (LLMs) are often pre-trained on large-scale multilingual texts, their reasoning abilities and real-world knowledge are mainly evaluated based on English datasets.Assessing LLM capabilities beyond English is increasingly vital but hindered due to the lack of suitable datasets.In this work, we introduce IndoMMLU, the first multi-task language understanding benchmark for Indonesian culture and languages, which consists of questions from primary school to university entrance exams in Indonesia.By employing professional teachers, we obtain 14,981 questions across 64 tasks and education levels, with 46% of the questions focusing on assessing proficiency in the Indonesian language and knowledge of nine local languages and cultures in Indonesia.Our empirical evaluations show that GPT-3.5 only manages to pass the Indonesian primary school level, with limited knowledge of local Indonesian languages and culture.Other smaller models such as BLOOMZ and Falcon perform at even lower levels.1 Fajri Koto, Nurul Aisyah, Haonan Li 0002, Timothy Baldwin |
EMNLP | 2 |