Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
This work addresses limitations in PEFT methods for ranking tasks, offering a more adaptive solution for text reranking applications.
The paper tackles the problem of fixed prompts and overfitting in Parameter Efficient Fine-Tuning (PEFT) for text reranking with Large Language Models by introducing a query-dependent approach (Q-PEFT) that uses query information to generate synthetic queries, achieving improved reranking performance as demonstrated on four public datasets.
Parameter Efficient Fine-Tuning (PEFT) methods have been extensively utilized in Large Language Models (LLMs) to improve the down-streaming tasks without the cost of fine-tuing the whole LLMs. Recent studies have shown how to effectively use PEFT for fine-tuning LLMs in ranking tasks with convincing performance; there are some limitations, including the learned prompt being fixed for different documents, overfitting to specific tasks, and low adaptation ability. In this paper, we introduce a query-dependent parameter efficient fine-tuning (Q-PEFT) approach for text reranking to leak the information of the true queries to LLMs and then make the generation of true queries from input documents much easier. Specifically, we utilize the query to extract the top-$k$ tokens from concatenated documents, serving as contextual clues. We further augment Q-PEFT by substituting the retrieval mechanism with a multi-head attention layer to achieve end-to-end training and cover all the tokens in the documents, guiding the LLMs to generate more document-specific synthetic queries, thereby further improving the reranking performance. Extensive experiments are conducted on four public datasets, demonstrating the effectiveness of our proposed approach.