Parallel Key-Value Cache Fusion for Position Invariant RAG

TL;DR

This paper introduces a framework for decoder-only models in RAG that achieves position invariance, ensuring consistent outputs regardless of context order, and demonstrates improved robustness in open domain question answering tasks.

Contribution

The proposed framework enables position-invariant RAG for decoder-only models, addressing the 'Lost in the Middle' problem and enhancing robustness against irrelevant passages.

Findings

Achieves position invariance in RAG models

Demonstrates superior robustness in question answering tasks

Outperforms existing approaches in handling irrelevant passages

Abstract

Recent advancements in Large Language Models (LLMs) underscore the necessity of Retrieval Augmented Generation (RAG) to leverage external information. However, LLMs are sensitive to the position of relevant information within contexts and tend to generate incorrect responses when such information is placed in the middle, known as `Lost in the Middle' phenomenon. In this paper, we introduce a framework that generates consistent outputs for decoder-only models, irrespective of the input context order. Experimental results for three open domain question answering tasks demonstrate position invariance, where the model is not sensitive to input context order, and superior robustness to irrelevent passages compared to prevailing approaches for RAG pipelines.