2 citations · 2 across the 5 of their papers we have counts for
7 papers
Large-scale factor analysis shows machine intelligence is only partially interpretable
Faiz Ghifari Haznitrama, Afrizal Hasbi Azizy, Faeyza Rishad Ardi
A common assumption in language model development is that cognitive abilities are organized around a general, domain-free intelligence factor, like fluid intelligence in humans. Th…
Evaluating Communicative Success in Machine-Translated Conversation
Faiz Ghifari Haznitrama, Alice Oh
Interpreter agents built on machine translation (MT) increasingly mediate live conversation between people who do not share a language, yet we still evaluate them with metrics buil…
A Neuropsychologically Grounded Evaluation of LLM Cognitive Abilities
Faiz Ghifari Haznitrama, Faeyza Rishad Ardi, Alice Oh
Large language models (LLMs) display a unified "general factor" of capability across 10 benchmarks (a finding confirmed by our factor analysis of 156 models), yet they still strugg…
OLA: Output Language Alignment in Code-Switched LLM Interactions
Juhyun Oh, Haneul Yoo, Faiz Ghifari Haznitrama +1
Code-switching, alternating between languages within a conversation, is natural for multilingual users, yet poses fundamental challenges for large language models (LLMs). When a us…
BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data
Jaap Jumelet, Abdellah Fourtassi, Akari Haga +23
We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally pla…
Survey of Cultural Awareness in Language Models: Text and Beyond
Siddhesh Pawar, Junyeong Park, Jiho Jin +7
Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ens…
Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese
Rifki Afina Putri, Faiz Ghifari Haznitrama, Dea Adhista +1
Large Language Models (LLMs) are increasingly being used to generate synthetic data for training and evaluating models. However, it is unclear whether they can generate a good qual…