Hacker News: Study: Large language models still lack general reasoning skills

Mar 7, 2025

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Source URL: https://santafe.edu/news-center/news/study-large-language-models-still-lack-general-reasoning-skills
Source: Hacker News
Title: Study: Large language models still lack general reasoning skills

Feedly Summary: Comments

AI Summary and Description: Yes

Summary: This text discusses research findings on the reasoning capabilities of large language models (LLMs) like GPT-4. It highlights the limitations of these models in understanding and solving complex analogy puzzles as compared to humans, suggesting that claims about their reasoning abilities may be overstated and emphasizing the need for robust testing methods in AI.

Detailed Description: The text addresses the following key points regarding GPT-4 and its reasoning capabilities:

– **Nature of Large Language Models**: GPT-4 and similar models are primarily designed to generate text based on training data. However, recent studies raise questions about their reasoning abilities.

– **Research Insight**: Conducted by SFI researchers Melanie Mitchell and Martha Lewis, the study explored the reasoning competence of GPT-4 by experimenting with modified analogy puzzles. The changes challenged the models in ways that traditional formats did not.

– **Human vs. Model Performance**:
– Humans consistently outperformed GPT models on these tasks, especially in modified scenarios like fictional alphabets.
– The models struggled with solving analogies when puzzles were adjusted, revealing potential deficiencies in general reasoning skills which are crucial for human-like cognition.

– **Importance of Robustness**:
– The researchers argue for the necessity of developing robustness tests that measure how well AI systems cope with unfamiliar problems. This could provide a clearer picture of the models’ trustworthiness.
– The implications are profound for AI applications, particularly in critical areas where reasoning and adaptability are paramount.

– **Call for Testing Standards**: The authors stressed the need for agreed-upon benchmark tasks to assess robustness, noting that without such measures, users may lack a reliable means of gauging the capabilities of LLMs.

In summary, the research provides vital insights into the limitations of current AI models concerning reasoning, emphasizing that individuals looking to implement AI solutions must be critically aware of these restrictions. The findings could inform compliance, governance, and development strategies for AI applications, highlighting the necessity of stringent evaluation measures in security and operational integrity contexts.

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