Rectifying Demonstration Shortcut in In-Context Learning: Introducing In-Context Calibration

Core Concepts

Rectifying the reliance of Large Language Models on pre-trained semantic priors in demonstrations through In-Context Calibration.

Abstract

Large language models often rely on pre-trained semantic priors, termed as the 'Demonstration Shortcut', hindering their ability to learn new input-label relationships. The proposed method, In-Context Calibration, addresses this issue by estimating semantic priors from demonstrations. Experimental results show significant improvements in performance across various tasks and model sizes.

Stats

Large language models can perform tasks with few demonstrations (Brown et al., 2020). LLMs rely on pre-trained knowledge for task learning (Reynolds and McDonell, 2021). Previous works focused on improving ICL prediction instabilities (Holtzman et al., 2021).

Quotes

"In this work, we term this phenomenon as the ‘Demonstration Shortcut’." "To tackle this problem, we propose In-Context Calibration, a method designed to rectify the Demonstration Shortcut in ICL." "Our proposed method not only demonstrated enhanced performance across various tasks but also showed improvement in task learning abilities."

Key Insights Distilled From

Rectifying Demonstration Shortcut in In-Context Learning

by Joonwon Jang... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09488.pdf

Deeper Inquiries

How can the Demonstration Shortcut impact real-world applications of Large Language Models?

The Demonstration Shortcut can have significant implications for real-world applications of Large Language Models (LLMs). When LLMs rely heavily on their pre-trained semantic priors from demonstrations rather than learning new input-label relationships, it can lead to inaccurate predictions and limited adaptability. In practical scenarios such as natural language understanding tasks, sentiment analysis, or hate speech detection, this reliance on semantic priors may result in biased or incorrect outputs. For example, in sentiment analysis, if an LLM consistently associates certain words with positive or negative sentiments based on its pre-training data without considering the context provided by new demonstrations, it may misclassify sentiments in unseen text.

What are potential drawbacks or limitations of relying on pre-trained semantic priors in demonstrations?

Relying solely on pre-trained semantic priors in demonstrations poses several drawbacks and limitations: Limited Adaptability: Depending too much on pre-trained knowledge restricts the model's ability to learn new patterns and adapt to different contexts presented by novel input-label pairs. Bias Amplification: The existing biases present in the pre-training data could be perpetuated and amplified when making predictions based solely on semantic priors without considering updated information from demonstrations. Reduced Generalization: Over-reliance on semantic priors may hinder the model's generalization capabilities across diverse datasets and tasks that require learning specific input-label mappings not covered during pre-training. Inflexibility: The model might struggle to override its initial assumptions about semantics even when presented with contradictory evidence from new examples.

How might advancements in In-Context Learning impact future developments in natural language processing?

Advancements in In-Context Learning offer promising opportunities for enhancing various aspects of natural language processing (NLP): Improved Task Performance: By rectifying issues like the Demonstration Shortcut through methods like In-Context Calibration, models can better learn new input-label relationships from demonstrations leading to improved task performance across a wide range of NLP tasks. Enhanced Adaptability: Models equipped with robust in-context learning abilities will be more adaptable to changing contexts and able to handle diverse datasets effectively. Reduced Bias: Advancements in overcoming reliance on pre-trained biases through calibration techniques can help mitigate bias amplification issues commonly observed in large language models. Better Generalization : Enhanced task learning abilities enable models to generalize better across different domains and tasks by focusing more on current contextual information rather than past knowledge alone. These advancements pave the way for more reliable and versatile NLP systems capable of handling complex linguistic tasks with greater accuracy and efficiency.