The Earth is Flat? Unveiling Factual Errors in Large Language Models
Wenxuan Wang Juluan Shi Zhaopeng Tu Youliang Yuan Jen-tse Huang Wenxiang Jiao Michael R. Lyu
Large Language Models (LLMs) like ChatGPT are foundational in various
applications due to their extensive knowledge from pre-training and
fine-tuning. Despite this, they are prone to generating factual and commonsense
errors, raising concerns in critical areas like healthcare, journalism, and
education to mislead users. Current methods for evaluating LLMs' veracity are
limited by test data leakage or the need for extensive human labor, hindering
efficient and accurate error detection. To tackle this problem, we introduce a
novel, automatic testing framework, FactChecker, aimed at uncovering factual
inaccuracies in LLMs. This framework involves three main steps: First, it
constructs a factual knowledge graph by retrieving fact triplets from a
large-scale knowledge database. Then, leveraging the knowledge graph,
FactChecker employs a rule-based approach to generates three types of questions
(Yes-No, Multiple-Choice, and WH questions) that involve single-hop and
multi-hop relations, along with correct answers. Lastly, it assesses the LLMs'
responses for accuracy using tailored matching strategies for each question
type. Our extensive tests on six prominent LLMs, including text-davinci-002,
text-davinci-003, ChatGPT~(gpt-3.5-turbo, gpt-4), Vicuna, and LLaMA-2, reveal
that FactChecker can trigger factual errors in up to 45\% of questions in these
models. Moreover, we demonstrate that FactChecker's test cases can improve
LLMs' factual accuracy through in-context learning and fine-tuning (e.g.,
llama-2-13b-chat's accuracy increase from 35.3\% to 68.5\%). We are making all
code, data, and results available for future research endeavors.