Challenge: CANDY is a benchmark to evaluate the capabilities and limitations of large language models (LLMs) for fact-checking misinformation.
Approach: a team of researchers develop a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in Chinese.
Outcome: CANDY is a benchmark to evaluate the capabilities and limitations of large language models in fact-checking misinformation in China.

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Challenge: Large language models (LLMs) have made significant advances in every natural language processing task, but they are vulnerable to small perturbations in the inputs, raising concerns about their robustness in the real world.
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Challenge: Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations.
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Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong (2024.naacl-long)

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Challenge: Large Language Models (LLMs) are increasingly used for accessing information on the web.
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Challenge: Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions.
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Challenge: Existing studies show that large language models are robust in commonsense reasoning . however, some variations in questions can lead to incorrect responses .
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Challenge: Existing studies have explored the role of Large Language Models in combating misinformation, but there is still a lack of detailed analysis on the specific aspects and extent to which LLMs are influenced by misinformation.
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Factuality of Large Language Models: A Survey (2024.emnlp-main)

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Challenge: Large language models (LLMs) are factually incorrect, which limits their applicability in real-world scenarios.
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Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)

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Challenge: Large language models generate naturally sounding answers over a broad range of human inquiries, but they often generate answers that contradict real-world facts.
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FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation (2025.acl-long)

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Challenge: Language models (LMs) generate false or unverifiable content, often known as hallucination, despite ongoing efforts to enhance their factuality.
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