Source URL: https://garymarcus.substack.com/p/llms-dont-do-formal-reasoning-and
Source: Hacker News
Title: LLMs don’t do formal reasoning – and that is a HUGE problem
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text discusses insights from a new article on large language models (LLMs) authored by researchers at Apple, which critically examines the limitations in reasoning capabilities of these models. It highlights the fragility of LLMs, where minor changes can significantly alter outcomes, thus questioning the reliability and robustness of current AI paradigms. For AI security professionals, these findings suggest a reevaluation of trust in LLM outputs, especially in applications requiring formal reasoning.
Detailed Description:
The article by Apple researchers presents significant critique and analysis of large language models (LLMs). It underscores various limitations, particularly in the realm of reasoning and reliability. Here are the major points discussed:
– **Limits of Formal Reasoning**: The researchers found no evidence of formal reasoning capabilities in LLMs; their outputs seemed better explained by advanced pattern matching.
– **Impact of Changes**: Results can shift significantly (by approximately 10%) with minor adjustments in input, highlighting LLMs’ fragility.
– **GSM-NoOp Task**: The team introduced a new task called GSM-NoOp, aimed at revealing flaws in LLM reasoning under distraction, echoing findings from previous related studies by other researchers.
– **Performance Decline**: Performance tends to degrade significantly when tasks scale, which has been observed in various model evaluations, including simple arithmetic.
– **Challenges in Abstract Reasoning**: This inability to maintain performance as complexity increases raises concerns about using LLMs in scenarios requiring complex logical reasoning, such as robotics and game planning, as evidenced by failures in chess rule adherence.
– **Neural Network Limitations**: The text posits that standard neural network architectures are insufficient for reliable extrapolation and formal reasoning. The author suggests integrating symbolic manipulation into LLM development to address these gaps.
– **Neurosymbolic AI**: Emphasizes the potential of combining traditional symbolic reasoning with neural networks to pave the way for more capable AI agents in the future.
– **Critical Stance on Future Promises**: The author expresses skepticism about the promises of resolving these fundamental issues in the short term, advocating for more rigorous approaches in AI research and development.
The information outlined has significant implications for professionals in AI security, as it highlights the vulnerabilities inherent in LLMs and emphasizes the necessity for careful application in critical areas requiring high reliability and formal reasoning.