Control Token with Dense Passage Retrieval

TL;DR

This paper improves retrieval-augmented generation in large language models by enhancing dense passage retrieval with control tokens, significantly reducing hallucinations and increasing retrieval accuracy.

Contribution

The novel integration of control tokens into the DPR model enhances retrieval accuracy, addressing limitations in existing retrieval-augmented generation methods.

Findings

13% improvement in Top-1 accuracy

4% improvement in Top-20 accuracy

Enhanced retrieval performance over standard DPR

Abstract

This study addresses the hallucination problem in large language models (LLMs). We adopted Retrieval-Augmented Generation(RAG) (Lewis et al., 2020), a technique that involves embedding relevant information in the prompt to obtain accurate answers. However, RAG also faced inherent issues in retrieving correct information. To address this, we employed the Dense Passage Retrieval(DPR) (Karpukhin et al., 2020) model for fetching domain-specific documents related to user queries. Despite this, the DPR model still lacked accuracy in document retrieval. We enhanced the DPR model by incorporating control tokens, achieving significantly superior performance over the standard DPR model, with a 13% improvement in Top-1 accuracy and a 4% improvement in Top-20 accuracy.