Evidence-Grounded Generation

Evidence-grounded generation is the practice of requiring generated claims to be supported by explicit evidence. It is stricter than retrieval-augmented generation: retrieval supplies candidate context, while grounding checks whether the final output is actually supported.

A generated answer can be viewed as claims:

$$y\to \mathcal{C}(y)=\{c_1,\dots ,c_n\}$$

Grounding asks whether each claim has support:

$$\forall c_i\in \mathcal{C}(y),\exists e_j\in \mathcal{E}\text{ such that }{e}_j\models c_i$$

where $\mathcal{E}$ is the evidence set and $\models$ means “supports.”

Evidence Types

• Retrieved document passage.
• Tool result.
• Source paper metadata.
• Local file or build output.
• Reproducible run record.
• Human-reviewed note.

Output Contract

A grounded answer should separate:

• Supported facts.
• Inferences from supported facts.
• Unverified claims marked to verify.
• Missing evidence.
• Next verification step.

Failure Modes

• Retrieved evidence is relevant but the answer adds unsupported claims.
• Evidence is present but outdated, low quality, or not primary.
• The model treats untrusted retrieved text as instructions.
• Citations point to sources that do not support the sentence.
• A fluent synthesis hides uncertainty or missing data.

Checks

• Which claims require evidence?
• Is the evidence primary enough for the claim?
• Are citations or wikilinks attached to the exact claim they support?
• Are unsupported claims narrowed or marked to verify?
• Is there a verification loop for high-impact content?

Related

• Hallucination and grounding
• Retrieval-augmented generation
• Embedding retrieval
• Evidence table
• Verification loop
• LLM Wiki