Source URL: https://arxiv.org/abs/2411.12580
Source: Hacker News
Title: Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The paper discusses how procedural knowledge in pretraining influences the reasoning capabilities of Large Language Models (LLMs). It reveals that while LLMs demonstrate proficiency in problem-solving, their reasoning is often inconsistent compared to human reasoning. The research highlights distinct generalization strategies employed by LLMs and the significance of procedural knowledge in their outputs.
Detailed Description: This study, authored by Laura Ruis and colleagues, dives into the intricate relationship between pretraining data and the reasoning abilities of LLMs. It aims to elucidate how these models manage generalization when faced with reasoning tasks, focusing on the following key areas:
– **Problem-Solving vs. Reasoning Gaps**:
– LLMs show a general ability to solve problems yet exhibit reasoning gaps, questioning their generalization strategies’ robustness.
– **Challenge of Traditional Measurement**:
– The vast amount of data used for training LLMs limits the application of traditional generalization checks like train-test set separation.
– **Investigation of Generalization Strategies**:
– The authors explore the data influencing LLM outputs during reasoning tasks by analyzing pretraining tokens from two models (7B and 35B parameters) and identifying key documents impacting their performance.
– **Distinct Influences for Factual vs. Reasoning Tasks**:
– The findings reveal that LLMs rely on mostly different data for factual queries compared to reasoning tasks. Notably, procedural knowledge is often involved in reasoning tasks, as documents influencing reasoning answers demonstrate step-wise solutions instead of direct answers.
– **Implications of Procedural Knowledge**:
– Procedural knowledge aids LLMs in synthesizing information and provides a more generalized strategy for handling reasoning tasks, contrasting with traditional retrieval-based approaches.
**Key Insights for AI Security and Compliance Professionals**:
– Understanding the limitations and reliance of LLMs on procedural knowledge can inform the development of more reliable AI systems, especially in scenarios requiring high accuracy and reasoning.
– The research opens pathways for improving the robustness of AI models, which is crucial for addressing compliance and security challenges in AI applications.
– In the realm of AI security, knowing how models handle reasoning can aid in the identification and mitigation of potential vulnerabilities and biases in AI behavior.
This paper provides a critical perspective on LLM functioning, with ramifications for security considerations in AI implementations, especially regarding trust and transparency in automated decision-making systems.