Large language models (LLMs) are increasingly used in high-stakes domains, yet their unfaithful or non-factual outputs create serious risks and hinder trustworthy deployment. Rigorous fact-checking is therefore essential to mitigate these risks. Most existing factuality verification methods improve evidence acquisition, but fail to capture complex logical relations between claims and multi-source evidence, especially when different evidence sources are inconsistent or incomplete. Moreover, current benchmarks rarely capture such realistic conflict scenarios. To address this, we introduce MAVEN, a real-world benchmark for verifying complex claims against heterogeneous and conflicting web evidence with fine-grained explanations. We further propose ArgCheck, an argumentation-based fact-checking framework. It models claims and retrieved evidence as arguments linked by attack relations, resolving conflicts through formal argumentation semantics. To avoid incomplete evidence, ArgCheck incorporates adversarial web retrieval to gather balanced multi-source evidence and grounds its verdicts and explanations in accepted arguments. Experiments on MAVEN and the public fact-checking benchmark AVeriTeC show that ArgCheck improves Macro-F1 over the strongest baseline by 5.39 and 11.10 points, respectively, while generating more faithful and evidence-grounded explanations. Our code and dataset are available online https://github.com/ZyraChen/ArgCheck.

The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.Yet, their effectiveness often diminishes in low-resource languages like Chinese, exacerbated by biased evaluations from data leakage, casting doubt on their true generalizability to new linguistic territories. In response, we introduce the Chinese Instruction-Following Benchmark (CIF-Bench), designed to evaluate the zero-shot generalizability of LLMs to the Chinese language. CIF-Bench comprises 150 tasks and 15,000 input-output pairs, developed by native speakers to test complex reasoning and Chinese cultural nuances across 20 categories. To mitigate data contamination, we release only half of the dataset publicly, with the remainder kept private, and introduce diversified instructions to minimize score variance, totaling 45,000 data instances.Our evaluation of 28 selected LLMs reveals a noticeable performance gap, with the best model scoring only 52.9%, highlighting the limitations of LLMs in less familiar language and task contexts.This work not only uncovers the current limitations of LLMs in handling Chinese language tasks but also sets a new standard for future LLM generalizability research, pushing towards the development of more adaptable, culturally informed, and linguistically diverse models.