7 papers
A Parallel Multi-Dialect Arabic Dependency Treebank
Noor Abo Mokh, Mohamed Adel, Jamila El-Gizuli +3
Dependency parsing resource development for Arabic has largely focused on Modern Standard Arabic, leaving dialectal Arabic comparatively under-resourced. We introduce MADARTB, the…
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…
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…
When Alignment Hurts: Decoupling Representational Spaces in Multilingual Models
Ahmed Elshabrawy, Hour Kaing, Haiyue Song +4
Alignment with high-resource standard languages is often assumed to aid the modeling of related low-resource varieties. We challenge this assumption by demonstrating that excessive…
Statement-Tuning Enables Efficient Cross-lingual Generalization in Encoder-only Models
Ahmed Elshabrawy, Thanh-Nhi Nguyen, Yeeun Kang +8
Large Language Models (LLMs) excel in zero-shot and few-shot tasks, but achieving similar performance with encoder-only models like BERT and RoBERTa has been challenging due to the…
Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context Learning
Jingcheng Niu, Subhabrata Dutta, Ahmed Elshabrawy +2
Large-scale Transformer language models (LMs) trained solely on next-token prediction with web-scale data can solve a wide range of tasks after seeing just a few examples. The mech…
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning
Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych +1
While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Ma…