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Faiz Ghifari Haznitrama

Faiz Ghifari Haznitrama has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Question answering, Reasoning; often with Alice Oh, Arnav Arora, Haneul Yoo.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 3 · 17 works · 59 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 93 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6245

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2021–2026 · 11 papersEducation 2021–2023 · Master, School of ComputingEmployment since 2024 · Ph.D. Student, School of Computingcurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksSafety Research · 1 workSocial Sciences · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksQuestion answering · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Survey of Cultural Awareness in Language Models: Text and Beyond (2025 · 106 citations)
    Large language models now sit behind chatbots, assistants, search and recommendation tools used around the world. Their training data, however, come mostly from Western sources. As a result, they can stereotype, flatten identities or respond insensitively to users from other backgrounds.
  2. Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese (2024 · 38 citations)
    The paper asks whether large language models can produce commonsense question-answering data that reflects local culture in Indonesian and in Sundanese.
  3. OLA: Output Language Alignment in Code-Switched LLM Interactions (2026 · 1 citation)
    Multilingual users often mix languages in prompts without saying which language they want the answer in. Humans infer this easily, but LLMs often reply in the wrong language. The authors introduce OLA, a Korean–English benchmark with two settings:

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Survey of Cultural Awareness in Language Models: Text and BeyondComputational LinguisticsSiddhesh Pawar, Junyeong Park, Jiho Jin, et al.2025 · 106 citations
  2. Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and SundaneseConference on Empirical Methods in Natural Language Processing (EMNLP)Rifki Afina Putri, Faiz Ghifari Haznitrama, Dea Adhista, et al.2024 · 38 citations
  3. OLA: Output Language Alignment in Code-Switched LLM InteractionsAnnual Meeting of the Association for Computational Linguistics (ACL)Juhyun Oh, Haneul Yoo, Faiz Ghifari Haznitrama, et al.2026 · 1 citation
  4. Evaluating Communicative Success in Machine-Translated Conversation ↗arXiv:2609.19885 · no summary yetFaiz Ghifari Haznitrama, Alice Oh2026

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 93 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

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