GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning

TL;DR

This work presents the first comprehensive comparison of various PEFT methods integrated with RAG, applied to both GPT and RETRO models, highlighting their relative performance.

Abstract

Parameter-Efficient Fine-Tuning (PEFT) and Retrieval-Augmented Generation (RAG) have become popular methods for adapting large language models while minimizing compute requirements. In this paper, we apply PEFT methods (P-tuning, Adapters, and LoRA) to a modified Retrieval-Enhanced Transformer (RETRO) and a baseline GPT model across several sizes, ranging from 823 million to 48 billion parameters. We show that RETRO models outperform GPT models in zero-shot settings due to their unique pre-training process but GPT models have higher performance potential with PEFT. Additionally, our study indicates that 8B parameter models strike an optimal balance between cost and performance and P-tuning lags behind other PEFT techniques. We further provide a comparative analysis between applying PEFT to an Instruction-tuned RETRO model and base RETRO model. This work presents the first comprehensive comparison of various PEFT methods integrated with RAG, applied to both GPT and RETRO models, highlighting their relative performance.

Figures (5)

• Figure 1: Average GPT vs RETRO scores of six datasets across model sizes of 823M to 48B parameters.
• Figure 2: Sample entry inputs and outputs from NQ dataset
• Figure 3: Comparison of Extra Large GPT and RETRO results averaged across 6 datasets.
• Figure 4: GPT vs RETRO comparisons on Extra Small and Medium sized models.
• Figure 5: GPT vs RETRO seperate method comparisons.