Exploring the Relationship between In-Context Learning and Instruction Tuning

TL;DR

This paper investigates the relationship between In-Context Learning and Instruction Tuning in large language models, revealing that ICL implicitly functions as a form of instruction tuning by analyzing hidden state changes.

Contribution

It demonstrates that ICL acts as implicit instruction tuning and explores factors influencing the convergence between ICL and IT in LLMs.

Findings

ICL changes hidden states similar to instruction tuning

Convergence depends on demonstration-related factors

Provides new insights into LLM behavior and paradigm connection

Abstract

In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications. However, they are significantly different. In ICL, a set of demonstrations are provided at inference time but the LLM's parameters are not updated. In IT, a set of demonstrations are used to tune LLM's parameters in training time but no demonstrations are used at inference time. Although a growing body of literature has explored ICL and IT, studies on these topics have largely been conducted in isolation, leading to a disconnect between these two paradigms. In this work, we explore the relationship between ICL and IT by examining how the hidden states of LLMs change in these two paradigms. Through carefully designed experiments conducted with LLaMA-2 (7B and 13B), we find that ICL is implicit IT. In other words, ICL changes an LLM's hidden states as if the demonstrations were used to instructionally tune the model. Furthermore, the convergence between ICL and IT is largely contingent upon several factors related to the provided demonstrations. Overall, this work offers a unique perspective to explore the connection between ICL and IT and sheds light on understanding the behaviors of LLM.