Investigating Reasoning Capabilities of Large Language Models
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models 🔗
This research investigates how Large Language Models (LLMs) utilize pretraining data to perform reasoning tasks, contrasting this with their approach to answering factual questions. The study reveals that while LLMs can solve problems, they often exhibit significant reasoning gaps compared to humans, raising questions about their generalization capabilities. The findings suggest that LLMs rely on procedural knowledge from pretraining documents, which influences reasoning tasks more consistently than the data used for factual questions. Specifically, it was found that the documents guiding reasoning tasks tend to share similar procedural knowledge, while those for factual questions are often unique. Additionally, the models depend less on specific documents for reasoning compared to factual retrieval, underscoring a more generalized approach to reasoning. This study highlights the importance of selecting high-quality data that demonstrates procedures across diverse reasoning tasks to enhance LLM performance.
- LLMs show both problem-solving abilities and reasoning gaps.
- Distinct sets of documents influence reasoning tasks versus factual questions.
- Procedural knowledge is crucial for reasoning, with similar influences across tasks.
- Documents for reasoning are less specific and more general than those for factual tasks.
- Findings suggest a need for careful data selection in LLM training.
What role does procedural knowledge play in LLM reasoning?
Procedural knowledge significantly influences how LLMs approach reasoning tasks, as they often rely on similar sets of documents that contain instructions or methods applicable to various queries.
How do LLMs differ in handling factual questions compared to reasoning tasks?
LLMs utilize distinct data for factual questions, often retrieving specific answers, while they apply a more generalized strategy for reasoning tasks, drawing from a broader range of documents.
Why is it important to select high-quality data for LLM training?
High-quality data that showcases procedures across different reasoning tasks can improve the models' reasoning capabilities and help address their limitations in understanding complex problems.