From Parameters to Performance: A Data-Driven Study on LLM Structure and Development

ACL ARR 2025 May Submission5551 Authors

Abstract: Large language models (LLMs) have achieved remarkable success across various domains, driving significant technological advancements and innovations. Despite the rapid growth in model scale and capability, systematic, data-driven research on how structural configurations affect performance remains scarce. To address this gap, we present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks. Leveraging this dataset, we conduct a systematic, data mining-driven analysis to uncover the relationship between structural configurations and performance. Our study begins with a review of the historical development of LLMs and an exploration of potential future trends. We then analyze how various structural choices impact performance across benchmarks and validate our findings using mechanistic interpretability techniques. By providing data-driven insights into LLM optimization, our work aims to guide the targeted development and application of future models.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: Interpretability and Analysis of Models for NLP, Resources and Evaluation

Contribution Types: Model analysis & interpretability, Data resources, Data analysis

Languages Studied: English

Keywords: Interpretability and Analysis of Models for NLP, Resources and Evaluation

Submission Number: 5551