Large language models, such as OpenAI’s GPT-3 and GPT-4, have garnered attention for their remarkable abilities to perform tasks traditionally associated with human intelligence. These models have aced high school tests, the bar exam, and even parts of the United States Medical Licensing Examination. However, the interpretation and evaluation of these results remain contentious among researchers.
Natalie Shapira, a computer scientist at Bar-Ilan University, raises concerns about the current evaluation techniques for large language models. She argues that these techniques create the illusion of greater capabilities than actually exist. Other researchers, including Melanie Mitchell, an artificial intelligence researcher at the Santa Fe Institute, question the meaning behind testing machines using human intelligence tests. Mitchell highlights the issues surrounding anthropomorphizing and the potential biases it introduces into testing.
The challenge lies in how we interpret the results of these tests. Human assessments, such as high school exams and IQ tests, assume that high scores reflect genuine knowledge, understanding, or cognitive skills relevant to the tested domain. However, it is unclear what these tests measure when applied to large language models. Is it true understanding, statistical patterns, or mere repetition?
Laura Weidinger, a senior research scientist at Google DeepMind, emphasizes that human psychology tests may not be suitable for evaluating large language models. These tests rely on assumptions that may not hold in the context of these models. While GPT-3 demonstrated superior performance to undergrads on some tests, it produced absurd results on others, failing to grasp analogical reasoning involving physical objects.
As the hype machine predicts large language models’ potential to replace white-collar jobs, it is imperative to critically analyze their capabilities. Researchers advocate for more rigorous and exhaustive evaluation methods, moving away from scoring machines on human tests. The field calls for a comprehensive understanding of what these models can and cannot do, separating the anthropomorphic bias from objective assessment.
Q: What are large language models?
A: Large language models, such as GPT-3 and GPT-4, are neural networks trained to predict the next word in a block of text, exhibiting impressive language generation and problem-solving abilities.
Q: Are large language models being tested like humans?
A: Yes, current evaluation techniques often involve assessing these models using tests designed for humans, such as high school exams and IQ tests. However, the interpretation of their performance on these tests is a subject of debate.
Q: What are the criticisms of the current evaluation techniques?
A: Critics argue that these techniques can create an illusion of greater capabilities, and that human intelligence tests may not be suitable for evaluating large language models due to underlying assumptions and biases.
Q: Why is it important to reevaluate the evaluation methods?
A: With concerns over the potential job displacement caused by large language models, it is crucial to have an accurate understanding of their capabilities. Researchers advocate for more rigorous evaluation methods to truly grasp what these models can accomplish.