ADAPT: Hybrid Prompt Optimization for LLM Feature Visualization
arXiv:2602.17867v1 Announce Type: cross Abstract: Understanding what features are encoded by learned directions in LLM activation space requires identifying inputs that strongly activate them. Feature visualization, which optimizes inputs to maximally activate a target direction, offers an alternative to costly dataset search approaches, but remains underexplored for LLMs due to the discrete nature of text. Furthermore, existing prompt optimization techniques are poorly suited to this domain, which is highly prone to local minima. To overcome these limitations, we introduce ADAPT, a hybrid method combining beam search initialization with adaptive gradient-guided mutation, designed around these failure modes. We evaluate on Sparse Autoencoder latents from Gemma 2 2B, proposing metrics grounded in dataset activation statistics to enable rigorous comparison, and show that ADAPT consistently outperforms prior methods across layers and latent types. Our results establish that feature visua
arXiv:2602.17867v1 Announce Type: cross Abstract: Understanding what features are encoded by learned directions in LLM activation space requires identifying inputs that strongly activate them. Feature visualization, which optimizes inputs to maximally activate a target direction, offers an alternative to costly dataset search approaches, but remains underexplored for LLMs due to the discrete nature of text. Furthermore, existing prompt optimization techniques are poorly suited to this domain, which is highly prone to local minima. To overcome these limitations, we introduce ADAPT, a hybrid method combining beam search initialization with adaptive gradient-guided mutation, designed around these failure modes. We evaluate on Sparse Autoencoder latents from Gemma 2 2B, proposing metrics grounded in dataset activation statistics to enable rigorous comparison, and show that ADAPT consistently outperforms prior methods across layers and latent types. Our results establish that feature visualization for LLMs is tractable, but requires design assumptions tailored to the domain.
Executive Summary
This article presents ADAPT, a novel hybrid method for optimizing prompts to maximize activation of target directions in Large Language Model (LLM) activation spaces. ADAPT combines beam search initialization with adaptive gradient-guided mutation to overcome the limitations of existing prompt optimization techniques in the discrete text domain. The authors evaluate ADAPT on Sparse Autoencoder latents from Gemma 2 2B, proposing metrics grounded in dataset activation statistics to enable rigorous comparison. The results demonstrate that ADAPT consistently outperforms prior methods across layers and latent types, establishing the tractability of feature visualization for LLMs. However, the approach requires design assumptions tailored to the domain, underscoring the need for further research.
Key Points
- ▸ ADAPT is a hybrid method combining beam search initialization with adaptive gradient-guided mutation
- ▸ ADAPT outperforms prior methods across layers and latent types
- ▸ The approach requires design assumptions tailored to the LLM domain
Merits
Improved Performance
ADAPT's hybrid approach and adaptive gradient-guided mutation enable it to consistently outperform prior methods across layers and latent types, demonstrating improved performance in feature visualization for LLMs.
Domain-Specific Design
The authors' emphasis on design assumptions tailored to the LLM domain highlights the importance of understanding the specific characteristics of the language model in developing effective feature visualization techniques.
Demerits
Limited Generalizability
The evaluation of ADAPT is limited to Sparse Autoencoder latents from Gemma 2 2B, which may not generalize to other LLMs or activation spaces, highlighting the need for further research to establish the broader applicability of the approach.
Expert Commentary
The article presents a valuable contribution to the field of language model interpretability and feature visualization. The authors' focus on the discrete nature of text and the limitations of existing prompt optimization techniques highlights the need for domain-specific design assumptions in developing effective feature visualization techniques for LLMs. While the evaluation of ADAPT is limited, the results demonstrate the potential of the approach, and further research is warranted to establish its broader applicability. The study's emphasis on rigorous comparison using metrics grounded in dataset activation statistics is particularly noteworthy, as it provides a more nuanced understanding of the performance of different methods in this domain.
Recommendations
- ✓ Recommendation 1: Future research should focus on evaluating ADAPT on a broader range of LLMs and activation spaces to establish its generalizability.
- ✓ Recommendation 2: The development of more effective language model applications informed by ADAPT's improved performance in feature visualization for LLMs should be a priority, particularly in domains where transparency and accountability are critical, such as healthcare or finance.
