WASD: Locating Critical Neurons as Sufficient Conditions for Explaining and Controlling LLM Behavior
arXiv:2603.18474v1 Announce Type: new Abstract: Precise behavioral control of large language models (LLMs) is critical for complex applications. However, existing methods often incur high training costs, lack natural language controllability, or compromise semantic coherence. To bridge this gap, we propose WASD (unWeaving Actionable Sufficient Directives), a novel framework that explains model behavior by identifying sufficient neural conditions for token generation. Our method represents candidate conditions as neuron-activation predicates and iteratively searches for a minimal set that guarantees the current output under input perturbations. Experiments on SST-2 and CounterFact with the Gemma-2-2B model demonstrate that our approach produces explanations that are more stable, accurate, and concise than conventional attribution graphs. Moreover, through a case study on controlling cross-lingual output generation, we validated the practical effectiveness of WASD in controlling model b
arXiv:2603.18474v1 Announce Type: new Abstract: Precise behavioral control of large language models (LLMs) is critical for complex applications. However, existing methods often incur high training costs, lack natural language controllability, or compromise semantic coherence. To bridge this gap, we propose WASD (unWeaving Actionable Sufficient Directives), a novel framework that explains model behavior by identifying sufficient neural conditions for token generation. Our method represents candidate conditions as neuron-activation predicates and iteratively searches for a minimal set that guarantees the current output under input perturbations. Experiments on SST-2 and CounterFact with the Gemma-2-2B model demonstrate that our approach produces explanations that are more stable, accurate, and concise than conventional attribution graphs. Moreover, through a case study on controlling cross-lingual output generation, we validated the practical effectiveness of WASD in controlling model behavior.
Executive Summary
This article proposes a novel framework called WASD, which aims to explain and control the behavior of large language models (LLMs) by identifying sufficient neural conditions for token generation. The framework, which represents candidate conditions as neuron-activation predicates, iteratively searches for a minimal set that guarantees the current output under input perturbations. The authors demonstrate the effectiveness of WASD through experiments on SST-2 and CounterFact with the Gemma-2-2B model, showcasing more stable, accurate, and concise explanations than conventional attribution graphs. A case study on controlling cross-lingual output generation further validates the practical effectiveness of WASD in controlling model behavior. This research has significant implications for the development of more controllable and reliable LLMs, particularly in complex applications.
Key Points
- ▸ WASD is a novel framework for explaining and controlling LLM behavior
- ▸ The framework identifies sufficient neural conditions for token generation
- ▸ Experiments demonstrate the stability, accuracy, and concision of WASD's explanations
Merits
Strength in explanation
WASD provides more stable, accurate, and concise explanations than conventional attribution graphs, making it a valuable tool for understanding LLM behavior.
Practical effectiveness
The framework's ability to control cross-lingual output generation demonstrates its practical effectiveness in real-world applications.
Demerits
Limited scope
The framework's effectiveness is demonstrated on a limited set of tasks and models, making it unclear whether WASD can generalize to other domains and applications.
Expert Commentary
While WASD demonstrates significant promise in explaining and controlling LLM behavior, its limitations in scope and generalizability highlight the need for further research and development. As the field continues to evolve, it is essential to consider the broader implications of WASD and its potential applications in real-world settings. Furthermore, the development of more robust and reliable LLMs, enabled by frameworks like WASD, underscores the importance of ongoing investment in AI research and development. Ultimately, the success of WASD and similar frameworks will depend on their ability to generalize across a wide range of tasks, models, and applications, and to address the complex challenges and trade-offs inherent in developing more controllable and explainable AI systems.
Recommendations
- ✓ Further research is needed to demonstrate the generalizability of WASD across a wider range of tasks and models.
- ✓ The development of more robust and reliable LLMs, enabled by frameworks like WASD, should be prioritized in ongoing AI research and development efforts.
