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20172026
most citedSparse Communication for Distributed Gradient Descent

552 citations · 816 across the 123 of their papers we have counts for

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25 papers · 1 filter

cs.CL2026

IndoNutriQA: An Indonesian Clinical Nutrition Question Answering Benchmark

Tirana Noor Fatyanosa, Ummu Ditya Erliana, Mahendra Data +3

Clinical Question Answering (QA) underpins Clinical Intelligent Decision Support Systems, but progress is severely limited for low-resource languages such as Indonesian by the abse…

cs.HC2026

AI Misconception and Adoption Among Indonesian K-12 Teachers

Alham Fikri Aji, Afifa Amriani, Rendi Chevi +2

Indonesia's fragmented geography and uneven infrastructure pose persistent challenges for adopting new educational technologies. While national initiatives aim to mainstream AI in…

cs.AI2026

HyperBrowseComp: A Multilingual and Multimodal Stress Test for Web-Browsing Agents

Alham Fikri Aji, Faiz Rizki Ramadhan, Zayd M. K. Zuhri +14

We introduce HyperBrowseComp, a multilingual and multimodal browsing benchmark comprising 423 manually authored and human-validated questions across 13 languages, written by native…

cs.CV2026

Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation

Patrick Amadeus Irawan, Iskandar Muda Rizky Parlambang, Rava Maulana +10

Video generation models are increasingly being explored as world simulators for embodied planning and learning. To do so effectively, these models must not only generate visually a…

cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

EDRAC: Benchmarking Arabic Dialect Reading Comprehension

Noor Abo Mokh, Kirill Chirkunov, Teresa Lynn +15

Dialectal Arabic (DA) remains under-resourced compared to Modern Standard Arabic (MSA), particularly for machine reading comprehension (MRC) and question answering (QA). Existing A…

cs.CL2026

DuDi: Dual-Signal Distillation with Cross-Lingual Verbalizer

Patomporn Payoungkhamdee, Tinnakit Udsa, Jian Gang Ngui +3

Small language models (SLMs) are efficient and scalable, but their multilingual capabilities degrade severely at sub-billion scales, especially for Southeast Asian (SEA) languages.…

cs.CL2026

TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering

Ryandito Diandaru, Ikhlasul Akmal Hanif, Fadli Aulawi Al Ghiffari +2

We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifying a text in-place to manifest…

cs.CL2026

SEA-NLI: Natural Language Inference as a Lens into Southeast Asian Cultural Understanding

Peerawat Chomphooyod, Jian Gang Ngui, Yosephine Susanto +5

Frontier LLMs perform well in Western contexts, but remain poorly tested on underrepresented cultures such as those in Southeast Asia (SEA). Existing NLI benchmarks are largely Wes…

cs.CL2026

Sense Representations Are Inducible Interfaces

Jan Christian Blaise Cruz, Alham Fikri Aji

Sense representations (explicit, per-token meaning decompositions) are useful for disambiguation, steering, and cross-lingual alignment, but existing approaches require models to b…

cs.DC2026

COPUS: Co-adaptive Parallelism and Batch Size Selection in Large Language Model Training

Akhmed Sakip, Erland Hilman Fuadi, Omar Sayedelahl +6

Training large language models requires jointly configuring two interdependent aspects of the system: the global batch size, which governs statistical efficiency, and the 3D parall…

cs.CV2026

LinguDistill: Recovering Linguistic Ability in Vision-Language Models via Selective Cross-Modal Distillation

Patrick Amadeus Irawan, Erland Hilman Fuadi, Shanu Kumar +2

Turning a pretrained language model (LM) into a vision-language model (VLM) through multimodal fine-tuning often erodes its native language ability, a form of catastrophic forgetti…

cs.CL2026

CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks

Peiqin Lin, Chenyang Lyu, Wenjiang Luo +22

Large language models (LLMs) are now deployed worldwide, inspiring a surge of benchmarks that measure their multilingual and multicultural abilities. However, these benchmarks prio…

cs.AI2026

Anthropogenic Regional Adaptation in Multimodal Vision-Language Model

Samuel Cahyawijaya, Peerat Limkonchotiwat, Tack Hwa Wong +45

While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedic…

cs.AI2026

LLM Olympiad: Why Model Evaluation Needs a Sealed Exam

Jan Christian Blaise Cruz, Alham Fikri Aji

Benchmarks and leaderboards are how NLP most often communicates progress, but in the LLM era they are increasingly easy to misread. Scores can reflect benchmark-chasing, hidden eva…

cs.CL2026

SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia

Panuthep Tasawong, Jian Gang Ngui, Alham Fikri Aji +2

Culturally aware safeguards are crucial for AI alignment in real-world settings, where safety extends beyond common sense and encompasses diverse local values, norms, and region-sp…

cs.CL2026

Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling

Alaa Elsetohy, Sama Hadhoud, Haryo Akbarianto Wibowo +4

Multilingual benchmarks rarely test reasoning over culturally grounded premises: translated datasets keep English-centric scenarios, while culture-first datasets often lack control…

cs.CL2026

PingPong: A Natural Benchmark for Multi-Turn Code-Switching Dialogues

Mohammad Rifqi Farhansyah, Hanif Muhammad Zhafran, Farid Adilazuarda +6

Code-switching is a widespread practice among the world's multilingual majority, yet few benchmarks accurately reflect its complexity in everyday communication. We present PingPong…

cs.CL2026

Idea First, Code Later: Disentangling Problem Solving from Code Generation in Evaluating LLMs for Competitive Programming

Sama Hadhoud, Alaa Elsetohy, Frederikus Hudi +3

Large Language Models (LLMs) increasingly succeed on competitive programming problems, yet existing evaluations conflate algorithmic reasoning with code-level implementation. We ar…

cs.CL2026

Multilinguality as Sense Adaptation

Jan Christian Blaise Cruz, David Ifeoluwa Adelani, Alham Fikri Aji

We approach multilinguality as sense adaptation: aligning latent meaning representations across languages rather than relying solely on shared parameters and scale. In this paper,…

cs.CL2026

Afri-MCQA: Multimodal Cultural Question Answering for African Languages

Atnafu Lambebo Tonja, Srija Anand, Emilio Villa-Cueva +16

Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering b…

cs.CL2026

Multicultural Spyfall: Assessing LLMs through a Dynamic Multilingual Social Deduction Game

Haryo Akbarianto Wibowo, Alaa Elsetohy, Qinrong Cui +1

The rapid advancement of Large Language Models (LLMs) has necessitated more robust evaluation methods that go beyond static benchmarks, which are increasingly prone to data saturat…

cs.CL2026

Does Visual Rendering Bypass Tokenization? Investigating Script-Tokenizer Misalignment in Pixel-Based Language Models

Lucky Susanto, Musa Izzanardi Wijanarko, Khumaisa Nur'aini +3

While pixel-based language modeling aims to bypass the sub-word tokenization bottleneck by rendering text as images, recent multimodal variants such as DualGPT reintroduce text tok…

cs.CL2026

Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning

Ahmed Attia, Alham Fikri Aji

Low-resource machine translation (MT) has gained increasing attention as parallel data from low-resource language communities is collected, but many approaches for improving low-re…