Large language models like ChatGPT, despite their sophistication, have shown inconsistencies in reasoning tests, as revealed in a recent study conducted by researchers at University College London. These models, which power generative AI platforms, gave varying answers when posed with the same reasoning test. Furthermore, even with additional context provided, the models did not exhibit any significant improvement in their ability to reason logically.
The study aimed to evaluate the capacity of these large language models (LLMs) to engage in rational reasoning. A rational agent, whether human or artificial, is expected to reason according to the rules of logic and probability. However, the results of the study indicated that many of the LLMs struggled with rational reasoning, often providing incorrect or inconsistent responses to the cognitive psychology tests.
The researchers employed a battery of 12 common tests from cognitive psychology to assess the reasoning abilities of the LLMs. These tests included tasks like the Wason task, the Linda problem, and the Monty Hall problem, which are known to challenge both human and artificial agents. The ability of humans to solve these tasks is generally low, with only a small percentage of participants achieving correct answers in previous studies.
The study revealed that the large language models exhibited irrationality in their responses, often making simple mistakes such as basic addition errors and confusion between consonants and vowels. For instance, the correct answers to the Wason task varied significantly among the different models, with some models achieving high accuracy while others performed poorly. These mistakes underscored the challenges in developing AI systems capable of logical reasoning.
Olivia Macmillan-Scott, the first author of the study, emphasized that despite improvements in models like GPT-4, large language models still do not “think” like humans. The study raised questions about the emergent behavior of these models and the implications of fine-tuning them to eliminate errors. Professor Mirco Musolesi highlighted the need to understand how these models reason and whether we want them to mimic human fallibility or strive for perfection.
Interestingly, some models refused to answer certain tasks on ethical grounds, a behavior attributed to safeguarding parameters that may not always function as intended. Additionally, providing additional context for the tasks did not consistently improve the responses of the LLMs. This underscores the complexity of developing AI systems that can reason logically and ethically in various situations.
The study sheds light on the limitations of large language models when it comes to rational reasoning. While these models have shown remarkable progress in generating text, images, and other content, their ability to engage in logical reasoning remains a significant challenge. As researchers continue to explore the capabilities and limitations of generative AI, it is essential to consider the implications of relying on these models for tasks that involve decision-making and problem-solving.
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