3 citations · 5 across the 4 of their papers we have counts for
6 papers
Terminal-Bench-LILT: Multilingual Agentic Coding Benchmark Grounded in Language, Region, and Culture
Yunsu Kim, Kaden Uhlig, Ashwin Purohit +10
Most evaluations for coding agents are conducted exclusively in English, which does not reflect real-world multilingual deployment. We present Terminal-Bench-LILT, a suite of 300 a…
Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems
Lia Shahnazaryan, Patrick Simianer, Joern Wuebker
We explore the impact of multi-source input strategies on machine translation (MT) quality, comparing GPT-4o, a large language model (LLM), with a traditional multilingual neural m…
Neural Machine Translation Models Can Learn to be Few-shot Learners
Raphael Reinauer, Patrick Simianer, Kaden Uhlig +2
The emergent ability of Large Language Models to use a small number of examples to learn to perform in novel domains and tasks, also called in-context learning (ICL). In this work,…
The Impact of Text Presentation on Translator Performance
Samuel Läubli, Patrick Simianer, Joern Wuebker +3
Widely used computer-aided translation (CAT) tools divide documents into segments such as sentences and arrange them in a side-by-side, spreadsheet-like view. We present the first…
Compact Personalized Models for Neural Machine Translation
Joern Wuebker, Patrick Simianer, John DeNero
We propose and compare methods for gradient-based domain adaptation of self-attentive neural machine translation models. We demonstrate that a large proportion of model parameters…
A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits
Sariya Karimova, Patrick Simianer, Stefan Riezler
The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written lan…