Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning
This work addresses the trustworthiness problem for users of LLMs in in-context learning, offering a novel evaluation method that is incremental in applying software testing principles to AI.
The paper tackles the vulnerability of large language models (LLMs) in in-context learning to adversarial perturbations and linguistic variations by introducing MMT4NL, a software testing-inspired framework that uses metamorphic adversarial examples to quantify bugs in prompts, revealing various linguistic bugs in state-of-the-art LLMs on sentiment analysis and question-answering tasks.
In-context learning (ICL) has emerged as a powerful capability of large language models (LLMs), enabling them to perform new tasks based on a few provided examples without explicit fine-tuning. Despite their impressive adaptability, these models remain vulnerable to subtle adversarial perturbations and exhibit unpredictable behavior when faced with linguistic variations. Inspired by software testing principles, we introduce a software testing-inspired framework, called MMT4NL, for evaluating the trustworthiness of in-context learning by utilizing adversarial perturbations and software testing techniques. It includes diverse evaluation aspects of linguistic capabilities for testing the ICL capabilities of LLMs. MMT4NL is built around the idea of crafting metamorphic adversarial examples from a test set in order to quantify and pinpoint bugs in the designed prompts of ICL. Our philosophy is to treat any LLM as software and validate its functionalities just like testing the software. Finally, we demonstrate applications of MMT4NL on the sentiment analysis and question-answering tasks. Our experiments could reveal various linguistic bugs in state-of-the-art LLMs.