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Muhammad Farid Adilazuarda

8 accepted papers

2025

MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding

NAACL 2025findings

Auto-regressive inference of transformers benefit greatly from Key-Value (KV) caching, but can lead to major memory bottlenecks as model size, batch size, and sequence length grow at scale. We introduce Multi-Layer Key-Value (MLKV) sharing, a novel approach extending KV sharing across transformer la…

2025

NusaAksara: A Multimodal and Multilingual Benchmark for Preserving Indonesian Indigenous Scripts

ACL 2025long

Indonesia is rich in languages and scripts. However, most NLP progress has been made using romanized text. In this paper, we present NusaAksara, a novel public benchmark for Indonesian languages that includes their original scripts. Our benchmark covers both text and image modalities and encompasses…

Cited by 0SourcePDFScholar
2025

WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines

NAACL 2025long

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate their understanding of such knowledge, we introduce WorldCuisines, a massive-scale benchmark for multilingual and multicul…

2024

CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark

NeurIPS 2024oral

Visual Question Answering~(VQA) is an important task in multimodal AI, which requires models to understand and reason on knowledge present in visual and textual data. However, most of the current VQA datasets and models are primarily focused on English and a few major world languages, with images th…

Cited by 34SourcePDFScholar
2024

Cultural Conditioning or Placebo? On the Effectiveness of Socio-Demographic Prompting

EMNLP 2024main

Socio-demographic prompting is a commonly employed approach to study cultural biases in LLMs as well as for aligning models to certain cultures. In this paper, we systematically probe four LLMs (Llama 3, Mistral v0.2, GPT-3.5 Turbo and GPT4) with prompts that are conditioned on culturally sensitive…

Cited by 7SourcePDFScholar
2024

LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization

EMNLP 2024finding

Pretrained language models (PLMs) have shown remarkable generalization toward multiple tasks and languages. Nonetheless, the generalization of PLMs towards unseen languages is poor, resulting in significantly worse language performance, or even generating nonsensical responses that are comparable to…

Cited by 3SourcePDFScholar
2024

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages

EMNLP 2024main

Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising…

2024

Towards Measuring and Modeling “Culture” in LLMs: A Survey

EMNLP 2024main

We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs). We observe that none of the studies explicitly define “culture, which is a complex, multifaceted concept; instead, they probe the models on some specially design…