Search Results for author: Jiahui Geng

Found 17 papers, 8 papers with code

Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update

no code implementations24 Jan 2025 Qing Li, Jiahui Geng, Zongxiong Chen, Kun Song, Lei Ma, Fakhri Karray

Vision-language models (VLMs) demonstrate strong multimodal capabilities but have been found to be more susceptible to generating harmful content compared to their backbone large language models (LLMs).

FIRE: Fact-checking with Iterative Retrieval and Verification

1 code implementation17 Oct 2024 Zhuohan Xie, Rui Xing, Yuxia Wang, Jiahui Geng, Hasan Iqbal, Dhruv Sahnan, Iryna Gurevych, Preslav Nakov

The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step.

Claim Verification Fact Checking +4

Reference-free Hallucination Detection for Large Vision-Language Models

no code implementations11 Aug 2024 Qing Li, Jiahui Geng, Chenyang Lyu, Derui Zhu, Maxim Panov, Fakhri Karray

In particular, we conduct an extensive study on three kinds of techniques: uncertainty-based, consistency-based, and supervised uncertainty quantification methods on four representative LVLMs across two different tasks.

Hallucination Question Answering +1

OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs

2 code implementations6 Aug 2024 Hasan Iqbal, Yuxia Wang, Minghan Wang, Georgi Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov

The increased use of large language models (LLMs) across a variety of real-world applications calls for automatic tools to check the factual accuracy of their outputs, as LLMs often hallucinate.

Fact Checking

CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark

no code implementations10 Jun 2024 David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo, Teresa Lynn, Injy Hamed, Aditya Nanda Kishore, Aishik Mandal, Alina Dragonetti, Artem Abzaliev, Atnafu Lambebo Tonja, Bontu Fufa Balcha, Chenxi Whitehouse, Christian Salamea, Dan John Velasco, David Ifeoluwa Adelani, David Le Meur, Emilio Villa-Cueva, Fajri Koto, Fauzan Farooqui, Frederico Belcavello, Ganzorig Batnasan, Gisela Vallejo, Grainne Caulfield, Guido Ivetta, Haiyue Song, Henok Biadglign Ademtew, Hernán Maina, Holy Lovenia, Israel Abebe Azime, Jan Christian Blaise Cruz, Jay Gala, Jiahui Geng, Jesus-German Ortiz-Barajas, Jinheon Baek, Jocelyn Dunstan, Laura Alonso Alemany, Kumaranage Ravindu Yasas Nagasinghe, Luciana Benotti, Luis Fernando D'Haro, Marcelo Viridiano, Marcos Estecha-Garitagoitia, Maria Camila Buitrago Cabrera, Mario Rodríguez-Cantelar, Mélanie Jouitteau, Mihail Mihaylov, Mohamed Fazli Mohamed Imam, Muhammad Farid Adilazuarda, Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Naome Etori, Olivier Niyomugisha, Paula Mónica Silva, Pranjal Chitale, Raj Dabre, Rendi Chevi, Ruochen Zhang, Ryandito Diandaru, Samuel Cahyawijaya, Santiago Góngora, Soyeong Jeong, Sukannya Purkayastha, Tatsuki Kuribayashi, Teresa Clifford, Thanmay Jayakumar, Tiago Timponi Torrent, Toqeer Ehsan, Vladimir Araujo, Yova Kementchedjhieva, Zara Burzo, Zheng Wei Lim, Zheng Xin Yong, Oana Ignat, Joan Nwatu, Rada Mihalcea, Thamar Solorio, Alham Fikri Aji

Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data.

Diversity Question Answering +1

OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs

4 code implementations9 May 2024 Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi Georgiev, Jiahui Geng, Preslav Nakov

To mitigate these issues, we propose OpenFactCheck, a unified framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document.

Benchmarking Fact Checking

Multimodal Large Language Models to Support Real-World Fact-Checking

no code implementations6 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.

Fact Checking

A Survey of Confidence Estimation and Calibration in Large Language Models

no code implementations14 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.

Language Modelling

A Survey on Dataset Distillation: Approaches, Applications and Future Directions

1 code implementation3 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.

Continual Learning Dataset Distillation +2

Towards General Deep Leakage in Federated Learning

1 code implementation18 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.

Federated Learning Image Restoration +1

DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities

no code implementations18 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.

Federated Learning Management

Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder

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.

Denoising Language Modeling +3

The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018

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).

Decoder Machine Translation +3

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