Question answering is an approach to retrieving information from a knowledge base using natural language. Within question answering systems that work over knowledge graphs (KGQA), a ranked list of SPARQL query candidates is typically computed for the given natural-language input, where the top-ranked query should reflect the intention and semantics of the given user’s question. This article follows our long-term research agenda of providing trustworthy KGQA systems by presenting an approach for filtering incorrect queries. Here, we employ (large) language models (LMs/LLMs) to distinguish between correct and incorrect queries. The main difference to the previous work is that we address here multilingual questions represented in major languages (English, German, French, Spanish, and Russian), and confirm the generalizability of the approach by also evaluating it on some low-resource languages (Ukrainian, Armenian, Lithuanian, Belarusian, and Bashkir). The considered LMs (BERT, DistilBERT, Mistral, Zephyr, GPT-3.5, and GPT-4) were applied to the KGQA systems – QAnswer (real-world system) and MemQA (idealized system) – as SPARQL query filters. The approach was evaluated on the multilingual dataset QALD-9-plus, which is based on the Wikidata knowledge graph. The experimental results imply that the considered KGQA systems achieve quality improvements for all languages when using our query-filtering approach.