Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change)

Abstract

Recent advances in large language models (LLMs) have transformed the field of natural language processing (NLP). From GPT-3 to PaLM, the state-of-the-art performance on natural language tasks is being pushed forward with every new large language model. Along with natural language abilities, there has been a significant interest in understanding whether such models exhibit reasoning capabilities with the use of reasoning benchmarks. However, even though results are seemingly positive, these benchmarks prove to be simplistic in nature and the performance of LLMs on these benchmarks cannot be used as evidence to support, many a times outlandish, claims being made about LLMs' reasoning capabilities. Further, these only represent a very limited set of simple reasoning tasks and we need to look at more sophisticated reasoning problems if we are to measure the true limits of such LLM-based systems. Motivated by this, we propose an extensible assessment framework to test the capabilities of LLMs on reasoning about actions and change, a central aspect of human intelligence. We provide multiple test cases that are more involved than any of the previously established benchmarks and each test case evaluates a different aspect of reasoning about actions and change. Results on GPT-3 (davinci), Instruct-GPT3 (text-davinci-002) and BLOOM (176B), showcase subpar performance on such reasoning tasks.Comment: An updated version of this work is here: arXiv:2302.06706 Accepted at Foundation Models for Decision Making Workshop at Neural Information Processing Systems, 202