COSTELLO: Contrastive Testing for Embedding-Based Large Language Model as a Service Embeddings

Abstract

Large language models have gained significant popularity and are often provided as a service (i.e., LLMaaS). Companies like OpenAI and Google provide online APIs of LLMs to allow downstream users to create innovative applications. Despite its popularity, LLM safety and quality assurance is a well-recognized concern in the real world, requiring extra efforts for testing these LLMs. Unfortunately, while end-to-end services like ChatGPT have garnered rising attention in terms of testing, the LLMaaS embeddings have comparatively received less scrutiny. We state the importance of testing and uncovering problematic individual embeddings without considering downstream applications. The abstraction and non-interpretability of embedded vectors, combined with the black-box inaccessibility of LLMaaS, make testing a challenging puzzle. This paper proposes COSTELLO, a black-box approach to reveal potential defects in abstract embedding vectors from LLMaaS by contrastive testing . Our intuition is that high-quality LLMs can adequately capture the semantic relationships of the input texts and properly represent their relationships in the high-dimensional space. For the given interface of LLMaaS and seed inputs, COSTELLO can automatically generate test suites and output words with potential problematic embeddings. The idea is to synthesize contrastive samples with guidance, including positive and negative samples, by mutating seed inputs. Our synthesis guide will leverage task-specific properties to control the mutation procedure and generate samples with known partial relationships in the high-dimensional space. Thus, we can compare the expected relationship (oracle) and embedding distance (output of LLMs) to locate potential buggy cases. We evaluate COSTELLO on 42 open-source (encoder-based) language models and two real-world commercial LLMaaS. Experimental results show that COSTELLO can effectively detect semantic violations, where more than 62% of violations on average result in erroneous behaviors (e.g., unfairness) of downstream applications.