The article further discusses the issues with interpreting the results of LLMs, as the assumptions made when humans score well on tests do not necessarily apply to machines. It also highlights the brittleness of LLM performance, where a small tweak to a test can drastically change the model's score. The article concludes by suggesting that to truly understand the intelligence of LLMs, researchers need to focus not just on test results, but also on understanding the mechanisms by which these models reason.
Key takeaways:
- Taylor Webb, a psychologist at the University of California, Los Angeles, has been studying the ability of OpenAI's GPT-3 and GPT-4 to solve abstract problems, and has found that they can pass a variety of tests designed to assess analogical reasoning, often scoring better than human undergraduates.
- However, there is debate among researchers about what these results actually mean, with some arguing that current evaluation techniques for large language models create an illusion of greater capabilities than actually exist, and that the practice of scoring machines on human tests should be abandoned.
- There is also concern about the brittleness of large language models' performance, as a small tweak to a test can drastically change the score, and the models often fail to correctly reason in situations that involve an extra few steps.
- Some researchers are calling for a shift in focus from test results to understanding the mechanisms by which large language models reason, and are exploring techniques used to study cognitive abilities in non-humans to avoid anthropomorphizing the models.