Knowledge-based Consistency Testing of Large Language Models
<|reference_start|>Knowledge-based Consistency Testing of Large Language Models: In this work, we systematically expose and measure the inconsistency and knowledge gaps of Large Language Models (LLMs). Specifically, we propose an automated testing framework (called KonTest) which leverages a knowledge graph to construct test cases. KonTest probes and measures the inconsistencies in the LLM's knowledge of the world via a combination of semantically-equivalent queries and test oracles (metamorphic or ontological oracle). KonTest further mitigates knowledge gaps via a weighted LLM model ensemble. Using four state-of-the-art LLMs (Falcon, Gemini, GPT3.5, and Llama2), we show that KonTest generates 19.2% error inducing inputs (1917 errors from 9979 test inputs). It also reveals a 16.5% knowledge gap across all tested LLMs. A mitigation method informed by KonTest's test suite reduces LLM knowledge gap by 32.48%. Our ablation study further shows that GPT3.5 is not suitable for knowledge-based consistency testing because it is only 60%-68% effective in knowledge construction.<|reference_end|>
@article{rajan2024knowledge-based,
    title={Knowledge-based Consistency Testing of Large Language Models},
    author={Sai Sathiesh Rajan, Ezekiel Soremekun, Sudipta Chattopadhyay},
    journal={arXiv preprint arXiv:2407.12830},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12830},
    primaryClass={cs.CL cs.AI cs.LG}
}