The application discloses a generative document reordering method based on retrieval enhancement generation, which comprises the following steps: firstly, preprocessing an original document set to obtain a candidate document set; then, inputting a query question and the candidate document set into a large
language model to generate answers and thought chains, disassembling the thought chains into atomic reasoning steps, and then performing sample screening; calculating the
information gain score and the
semantic similarity score of each atomic reasoning step for each candidate document, taking the maximum value as the final contribution
score of the document after weighted fusion, and sorting the candidate documents according to the score to form a training
data set; training a generative reordering model to make it output an ordered sequence of document identifiers; and applying the pre-trained generative reordering model in the online
inference stage. The method changes the sorting target to the actual contribution of the document to the reasoning step, combines a high-quality supervision
signal, constructs a sliding window global rearrangement strategy, and improves the retrieval accuracy of key documents and the practicability of the reordering model in a complex question and answer scene.