Evidence-Grounded Generation

Evidence-grounded generation studies how language models can use documents, databases, images, and other sources to produce claims that readers can verify. Common approaches include retrieval-augmented generation, claim-level verification, evidence graphs, citation alignment, and abstention when support is insufficient. Benchmarks increasingly test not only answer accuracy but also whether each claim is supported by the cited evidence, including in medical, scientific, legal, and long-form tasks. A central challenge remains that retrieving relevant evidence does not ensure models will integrate it faithfully, and stricter grounding can trade off against coverage, fluency, or efficiency.