collaborators

7 papers

cs.CL2026

CultureTalk-ID: A Multi-Task Dialogue Benchmark for Cultural Commonsense in Indonesian Local Languages

Muhammad Dehan Al Kautsar, Salsabila Pranida, Bilal Elbouardi +1

Culture is lived through conversation, yet existing Indonesian cultural commonsense benchmarks evaluate LLMs on short and isolated prompts, stripping away the dialogic context in w…

cs.CL2026

Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages

Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida +16

Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpreta…

cs.CL2026

Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures

Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377

To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…

cs.CL2026

A Parallel Cross-Lingual Benchmark for Multimodal Idiomaticity Understanding

Dilara Torunoğlu-Selamet, Dogukan Arslan, Rodrigo Wilkens +75

Potentially idiomatic expressions (PIEs) construe meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challen…

cs.CL2025

ASR Under Noise: Exploring Robustness for Sundanese and Javanese

Salsabila Zahirah Pranida, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi +1

We investigate the robustness of Whisper-based automatic speech recognition (ASR) models for two major Indonesian regional languages: Javanese and Sundanese. While recent work has…

cs.CL2025

Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese

Salsabila Zahirah Pranida, Rifo Ahmad Genadi, Fajri Koto

Culturally grounded commonsense reasoning is underexplored in low-resource languages due to scarce data and costly native annotation. We test whether large language models (LLMs) c…