28 citations · 44 across the 8 of their papers we have counts for
12 papers
Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following
Niklas Scholz, David Thulke, Abdallah Nasir +3
Fine-tuning large language models on parallel data improves translation quality but can cause catastrophic forgetting. Mitigation methods are generally evaluated by retention on ge…
Findings of the IWSLT 2024 Evaluation Campaign
Ibrahim Said Ahmad, Antonios Anastasopoulos, Ondřej Bojar +42
This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and off…
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
David Thulke, Yingbo Gao, Petrus Pelser +23
This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models fro…
AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Task
Patrick Wilken, Evgeny Matusov
To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-…
SubER: A Metric for Automatic Evaluation of Subtitle Quality
Patrick Wilken, Panayota Georgakopoulou, Evgeny Matusov
This paper addresses the problem of evaluating the quality of automatically generated subtitles, which includes not only the quality of the machine-transcribed or translated speech…
Neural Simultaneous Speech Translation Using Alignment-Based Chunking
Patrick Wilken, Tamer Alkhouli, Evgeny Matusov +1
In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency…