Compressed Sensing for Capability Localization in Large Language Models
arXiv:2603.03335v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a wide range of capabilities, including mathematical reasoning, code generation, and linguistic behaviors. We show that many capabilities are highly localized to small subsets of attention heads within Transformer architectures. Zeroing out as few as five task-specific heads can degrade performance by up to $65\%$ on standard benchmarks measuring the capability of interest, while largely preserving performance on unrelated tasks. We introduce a compressed sensing based method that exploits the sparsity of these heads to identify them via strategic knockouts and a small number of model evaluations. We validate these findings across Llama and Qwen models ranging from 1B to 8B parameters and a diverse set of capabilities including mathematical abilities and code generation, revealing a modular organization in which specialized capabilities are implemented by sparse, functionally distinct components. Over
arXiv:2603.03335v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a wide range of capabilities, including mathematical reasoning, code generation, and linguistic behaviors. We show that many capabilities are highly localized to small subsets of attention heads within Transformer architectures. Zeroing out as few as five task-specific heads can degrade performance by up to $65\%$ on standard benchmarks measuring the capability of interest, while largely preserving performance on unrelated tasks. We introduce a compressed sensing based method that exploits the sparsity of these heads to identify them via strategic knockouts and a small number of model evaluations. We validate these findings across Llama and Qwen models ranging from 1B to 8B parameters and a diverse set of capabilities including mathematical abilities and code generation, revealing a modular organization in which specialized capabilities are implemented by sparse, functionally distinct components. Overall, our results suggest that capability localization is a general organizational principle of Transformer language models, with implications for interpretability, model editing, and AI safety. Code is released at https://github.com/locuslab/llm-components.
Executive Summary
This article introduces a novel approach to capability localization in large language models (LLMs) using compressed sensing. By exploiting the sparsity of attention heads within Transformer architectures, the authors develop a method to identify task-specific heads via strategic knockouts and model evaluations. The findings, validated across LLMs ranging from 1B to 8B parameters, reveal a modular organization of specialized capabilities implemented by sparse, functionally distinct components. The research has far-reaching implications for LLM interpretability, model editing, and AI safety. The authors release code at https://github.com/locuslab/llm-components, enabling the scientific community to replicate and build upon their work.
Key Points
- ▸ Compressed sensing based method to identify task-specific attention heads in LLMs
- ▸ Sparsity of attention heads within Transformer architectures
- ▸ Modular organization of specialized capabilities in LLMs
Merits
Strength in Methodology
The use of compressed sensing provides a robust and efficient approach to identifying task-specific heads, leveraging the inherent sparsity of LLMs.
Strength in Interpretability
The research reveals a modular organization of LLM capabilities, enhancing our understanding of how LLMs process and generate language.
Demerits
Limitation in Generalizability
The study focuses on Transformer-based LLMs; it remains unclear whether the findings generalize to other LLM architectures.
Limitation in Scalability
The approach may not be scalable to very large LLMs, potentially requiring significant computational resources.
Expert Commentary
The article presents a thought-provoking exploration of LLM capabilities, leveraging the power of compressed sensing to uncover the modular organization of specialized components within these models. The findings are significant, and the methodology is robust. However, as with any pioneering research, there are limitations and areas for future investigation. The authors' decision to release code and data will undoubtedly facilitate further research in this area. This study has the potential to significantly impact the development of more interpretable, efficient, and safe LLMs.
Recommendations
- ✓ Future research should investigate the generalizability of the findings to other LLM architectures and explore potential applications in areas like natural language processing and computer vision.
- ✓ Researchers should work to develop more scalable approaches to capability localization, enabling the efficient analysis of very large LLMs.