7 citations · 20 across the 18 of their papers we have counts for
6 papers · 1 filter
FIRE: Fact-checking with Iterative Retrieval and Verification
Zhuohan Xie, Rui Xing, Yuxia Wang +5
Fact-checking long-form text is challenging, and it is therefore common practice to break it down into multiple atomic claims. The typical approach to fact-checking these atomic cl…
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
Hasan Iqbal, Yuxia Wang, Minghan Wang +4
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 ofte…
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
Mervat Abassy, Kareem Elozeiri, Alexander Aziz +21
The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written o…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
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 pres…
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs
Yuxia Wang, Minghan Wang, Hasan Iqbal +3
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. Difficulties lie…
Multimodal Large Language Models to Support Real-World Fact-Checking
Jiahui Geng, Yova Kementchedjhieva, Preslav Nakov +1
Multimodal large language models (MLLMs) carry the potential to support humans in processing vast amounts of information. While MLLMs are already being used as a fact-checking tool…