Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models

arXiv:2404.04522v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cost cs.ai cs.cl cs.ir cs.lg fine-tuning language language models large language large language models llms peft query ranking streaming studies tasks text type