Can RAG systems refuse to answer without reliable evidence?

A hybrid multilingual RAG system for question answering over noisy historical newspapers handles two kinds of corruption that modern RAG benchmarks largely ignore: OCR errors that scramble surface text and language drift where vocabulary and orthography shift across centuries within the same corpus. Its defense against both is structural rather than denoising. The pipeline uses semantic query expansion to widen what counts as a match, multi-query retrieval with Reciprocal Rank Fusion to consolidate evidence across query variants, and — most importantly — a grounded generation prompt that only produces answers when evidence is actually retrieved.

The grounded-refusal step is what distinguishes this from a typical noisy-RAG approach. When sources are corrupted, the temptation is for the generator to fill in the gaps from prior knowledge, which produces plausible-sounding but ungrounded answers. The grounded prompt makes refusal the default when retrieval fails, which preserves the integrity of the answer at the cost of coverage. Combined with the semantic and multi-query expansion that improves recall on degraded text, the system trades hallucination for honest "I cannot find this" responses. The cost of this trade is real: Does reasoning fine-tuning make models worse at declining to answer? shows that recent training trends actively work against this kind of refusal posture.

The general principle is that corruption-tolerant RAG should expand retrieval aggressively while constraining generation conservatively — recall up, but only generate when grounded. This inverts the implicit policy of most RAG systems, which is to retrieve narrowly and generate freely. For high-noise corpora the inversion is the correct trade.