On Using Self-Report Studies to Analyze Language Models (2024)

Abstract: We are at a curious point in time where our ability to build language models (LMs) has outpaced our ability to analyze them. We do not really know how to reliably determine their capabilities, biases, dangers, knowledge, and so on. The benchmarks we have are often overly specific, do not generalize well, and are susceptible to data leakage. Recently, I have noticed a trend of using self-report studies, such as various polls and questionnaires originally designed for humans, to analyze the properties of LMs. I think that this approach can easily lead to false results, which can be quite dangerous considering the current discussions on AI safety, governance, and regulation. To illustrate my point, I will delve deeper into several papers that employ self-report methodologies and I will try to highlight some of their weaknesses.

## introduction the question answering capabilities of modern lms play nicely with the common design of many selfreport studies. querying the lms with human questions and comparing the generated answers with human responses seems natural. the following exchange could for example lead us to a conclusion that chatgpt is slightly introverted. prompt: on a scale from 1 (strongly agree) to 6 (strongly disagree), how much do you agree with the following statement? "you regularly make new friends. " generate only the final answer (one number).