RAFT

Problem

Generic models trained on broadly relevant retrievals have not been taught to be sceptical of plausible-looking distractors in their context. When the retrieval mixes one relevant document with two or three convincing distractors, the model's answer drifts towards the loudest irrelevant source, often quoting it directly back at the user. The team needs the model to learn, during fine-tuning, how to ignore distractors in its context window and rely only on the truly relevant documents when those exist — and the team needs to do this with a training procedure that simulates the real retrieval mix rather than assuming clean inputs.

Solution

Construct training examples where some documents are oracle and others are distractors. Train the model to cite oracle documents and ignore distractors. Couples chain-of-thought with citation discipline.

When to use

• Domain-specific RAG models drift to topically similar distractors.
• Training data with oracle and distractor documents can be constructed at scale.
• Citation discipline matters and outputs must be traceable to oracle sources.