Abstracts: December 6, 2023

XIE: Yeah, in a sense, actually, we are exploring the potential of psychometrics to revolutionize how we evaluate general-purpose AI. Because AI is advancing at a very rapid pace, traditional evaluation methods face significant challenges, especially when it comes to predicting a model’s performance in unfamiliar scenarios. And this method also lacks a robust mechanism to assess their own quality. Additionally, we, in this paper, we delve into the complexity of directly applying psychometrics to this domain and underscore several promising directions for future research. We believe that this research is of great importance. As AI continues to be integrated into novel application scenarios, it could have significant implications for both individuals and society at large. It’s crucial that we ensure their performance is both consistent and reliable.

HUIZINGA: OK, so I’m going to drill in a little bit in case there’s people in our audience that don’t understand what psychometrics is. Could you explain that a little bit for the audience? 

XIE: Yeah, psychometrics could be considered as a subdomain of psychology. Basically, psychology just studies everything about humans, but psychometrics is specifically developed to study how we can better evaluate, we could also call this general-purpose intelligence, but it’s human intelligence. So there are, actually, a lot of methodologies and approaches in how we develop this kind of test and what tasks we need to carry out. The previous AI is designed for specific tasks like machine translation, like summarization. But now I think people are already aware of many progress in big models, in large language models. AI, actually, currently can be considered as some kind of solving general-purpose tasks. Sometimes we call it few-shot learning, or sometimes we call it like zero-shot learning. We don’t need to train a model before we bring new tasks to them. So this brings a question in how we evaluate this kind of general-purpose AI, because traditionally, we evaluate AI usually using some specific benchmark, specific dataset, and specific tasks. This seems to be unsuitable to this new general-purpose AI. 

HUIZINGA: So how does your approach build on and/or differ from what’s been done previously in this field? 

XIE: Yeah, we actually see a lot of efforts have been investigated into evaluating the performance of these new large language models. But we see a significant portion of these evaluations are task specific. They’re still task specific. And also, frankly speaking, they are easily affected by changes. That means even slight alterations to a test could lead to substantial drops in performance. So our methodology differs from these approaches in that rather than solely testing how AI performs on those predetermined tasks, we actually are evaluating those latent constructs because we believe that pinpointing these latent constructs is very important.

HUIZINGA: Yeah. 

XIE: It’s important in forecasting AI’s performance in evolving and unfamiliar contexts. We can use an example like game design. With humans, even if an individual has never worked on game design—it’s just a whole new task for her—we might still confidently infer their potential if we know they possess the essential latent constructs, or abilities, which are important for game design. For example, creativity, critical thinking, and communication. 

HUIZINGA: So this is a vision paper and you’re making a case for using psychometrics as opposed to regular traditional benchmarks for assessing AI. So would you say there was a methodology involved in this as a research paper, and if so, how did you conduct the research for this? What was the overview of it? 

XIE: As you said, this is a vision paper. So instead of describing a specific methodology, we are collaborating with several experienced psychometrics researchers. Collectively, we explore the feasibility of integrating psychometrics into AI evaluation and discerning which concepts are viable and which are not. In February this year, we hosted a workshop on this topic. Over the past months, we have engaged in, in numerous discussions, and the outcome of these discussions is articulated in this paper. And additionally, actually, we are also in the middle of drafting another paper; that paper will apply insights from this paper to devise a rigorous methodology for assessing the latent capability of the most cutting-edge language models. 

HUIZINGA: When you do a regular research paper, you have findings. And when you did this paper and you workshopped it, what did you come away with in terms of the possibilities for what you might do on assessing AI with psychometrics? What were your major findings? 

XIE: Yeah, our major findings can be divided into two areas. First, we underscore the significant potential of psychometrics. This includes exploring how these metrics can be utilized to enhance predictive accuracy and guarantee test quality. Second, we also draw attention to the new challenges that arise when directly applying these principles to AI. For instance, test results could be misinterpreted, as assumptions verified for human tests might not necessarily apply to AI. Furthermore, capabilities that are essential for humans may not hold the same importance for AI.

HUIZINGA: Hmm …  

XIE: Another notable challenge is the lack of a consistent and defined population of AI, especially considering their rapid evolution. But this population is essential for traditional psychometrics, and we need to have a population of humans for verifying either the reliability or the validity of a test. But for AI, this becomes a challenge. 

HUIZINGA: Based on those findings, how do you think your work is significant in terms of real-world impact at this point? 

XIE: We believe that our approach will signal the start of a new era in the evaluation of general-purpose AI, shifting from earlier, task-specific methodologies to a more rigorous scientific method. Fundamentally, there’s an urgent demand to establish a dedicated research domain focusing solely on AI evaluation. We believe psychometrics will be at the heart of this domain. Given AI’s expanding role in society and its growing significance as an indispensable assistant, this evolution will be crucial. I think one missing part of current AI evaluation is how we can make sure the test, the benchmark, or these evaluation methods of AI themselves, is scientific. Actually, previously, I used the example of game design. Suppose in the future, I think there are a lot of people discussing language model agents, AI agents … they could be used to not only write in code but also develop software by collaborating among different agents. Then what kind of capabilities, or we call them latent constructs, of these AI models they should have before they make success in game design or any other software development. For example, like creativity, critical thinking, communication. Because this could be important when there are multiple AI models—they communicate with each other, they check the result of the output of other models. 

HUIZINGA: Are there other areas that you could say, hey, this would be a relevant application of having AI evaluated with psychometrics instead of the regular benchmarks because of the generality of intelligence?

XIE: We are mostly interested in maybe doing research, because a lot of researchers have started to leverage AI for their own research. For example, not only for writing papers, not only for generating some ideas, but maybe they could use AI models for more tasks in the whole pipeline of research. So this may require AI to have some underlying capabilities, like, as we have said, like critical thinking—how AI should define the new ideas and how they check whether these ideas are feasible and how they propose creative solutions and how they work together on research. This could be another domain. 

HUIZINGA: So if there was one thing that you want our listeners to take away from this work, what would it be? 

XIE: Yeah, I think the one takeaway I want to say is we should be aware of the vital importance of AI evaluation. We are still far from achieving a truly scientific standard, so we need to still work hard to get that done. 

HUIZINGA: Finally, what unanswered questions or unsolved problems remain in this area? What’s next on your research agenda that you’re working on? 

XIE: Yeah, actually, there are a lot of unanswered questions as highlighted at the later part of this paper. Ultimately, our goal is to adapt psychometric theories and the techniques to fit AI contexts. So we have discussed with our collaborators in both AI and psychometrics … some examples would be, how can we develop guidelines, extended theories, and techniques to ensure a rigorous evaluation that prevents misinterpretation? And how can we best evaluate assistant AI and the dynamics of AI-human teaming? This actually is particularly proposed by one of our collaborators in the psychometrics domain. And how do we evaluate the value of general-purpose AI and ensure their alignment with human objectives? And then how can we employ semiautomatic methods to develop psychometric tests, theories, and techniques with the help of general-purpose AI? That means we use AI to solve these problems by themselves. This is also important because, you know, psychometrics or psychology have developed for hundreds, or maybe thousands, of years to come to all the techniques today. But can we shorten that period? Can we leverage AI to speed up this development? 

HUIZINGA: Would you say there’s wide agreement in the AI community that this is a necessary direction to head?

XIE: This is only starting. I think there are several papers discussing how we can apply some part of psychology or some part of psychometrics to AI. But there is no systematic discussion or thinking along this line. So I, I don’t think there is agreement, but there’s already initial thoughts and initial perspectives showing in the academic community. 

[MUSIC PLAYS]

HUIZINGA: Well, Xing Xie, thanks for joining us today, and to our listeners, thank you for tuning in. If you’re interested in learning more about this paper, you can find a link at aka.ms/abstracts (opens in new tab), or you can find a preprint of the paper on arXiv. See you next time on Abstracts!