Training the model to decide when to retrieve and to critique whether passages support its output improves factuality and citation accuracy.

Capability: Checking claims against evidence

Sources

supportsSelf-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Training the model to decide when to retrieve and to critique whether passages support its output improves factuality and citation accuracy.

Reviewed by AI and Russ MillerLast checked: 2026-09-03Evidence activity

Either way, bring evidence: a link, or what you saw.

Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.

Related claims

When an 8B model verifies natural-language claims against knowledge-graph triples, scoring the evidence twice under opposing prompts (argue it supports, argue it refutes) and adjudicating between the two arguments avoids the bias toward a "Supported" verdict that a single-perspective assessment shows, and is the largest single contributor to accuracy in that pipeline.Checking claims against evidence · reviewed by AI
When new knowledge is a set of interconnected fictional future events rather than isolated fact edits, updating 4-8B instruct models by mid-training on teacher-generated long-form articles plus preference tuning (preferring reasoned answers over outdated, hallucinated, or needlessly abstaining ones) beats graph-retrieval and in-context editing baselines on multi-hop and causal questions, while remaining worse than in-context methods on exact numeric single-hop recall unless retrieved facts are also supplied.Absorbing and flexibly using new knowledge · reviewed by AI
In LLM fact-checking over supplied evidence, high veracity accuracy does not imply the verdict depends on that evidence: when the model's own cited sentences are iteratively removed, off-the-shelf models (GPT-4o-mini, Gemini-2.5-Flash-Lite, Qwen-2.5-32B, Llama-3.1-8B) keep their SUPPORT/REFUTE labels at a non-zero plateau instead of abstaining, and training with paired supervision that requires NOT_ENOUGH_INFO on the evidence-ablated input removes most of that persistence without losing accuracy.Checking claims against evidence · reviewed by AI
Grounding generation in retrieved documents improves factual accuracy on knowledge-intensive tasks.Stating false facts confidently
Models readily adopt a single counter-memory passage when it is coherent, and when sources conflict they follow the majority and show confirmation bias toward their own beliefs.Checking claims against evidence