no code implementations • 6 Mar 2024 • Jiahui Geng, Yova Kementchedjhieva, Preslav Nakov, Iryna Gurevych
To the best of our knowledge, we are the first to evaluate MLLMs for real-world fact-checking.
1 code implementation • 15 Nov 2023 • Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
no code implementations • 14 Nov 2023 • Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, Iryna Gurevych
Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
no code implementations • 5 May 2023 • Zongxiong Chen, Jiahui Geng, Derui Zhu, Herbert Woisetschlaeger, Qing Li, Sonja Schimmler, Ruben Mayer, Chunming Rong
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset.
1 code implementation • 3 May 2023 • Jiahui Geng, Zongxiong Chen, Yuandou Wang, Herbert Woisetschlaeger, Sonja Schimmler, Ruben Mayer, Zhiming Zhao, Chunming Rong
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.
no code implementations • 18 Oct 2021 • Jiahui Geng, Yongli Mou, Feifei Li, Qing Li, Oya Beyan, Stefan Decker, Chunming Rong
We find that image restoration fails even if there is only one incorrectly inferred label in the batch; we also find that when batch images have the same label, the corresponding image is restored as a fusion of that class of images.
no code implementations • 18 May 2021 • Jiahui Geng, Neel Kanwal, Martin Gilje Jaatun, Chunming Rong
DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process.
no code implementations • EMNLP 2018 • Yunsu Kim, Jiahui Geng, Hermann Ney
Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences.
no code implementations • WS 2018 • Miguel Gra{\c{c}}a, Yunsu Kim, Julian Schamper, Jiahui Geng, Hermann Ney
This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018).