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Instruction Following by Boosting Attention of Large Language Models

Created by
  • Haebom

Author

Vitoria Guardieiro, Adam Stein, Avishree Khare, Eric Wong

Outline

This paper emphasizes the importance of generative control for safe and reliable deployment of large-scale language models (LLMs), and introduces the research trend of latent steering, a lightweight technique, in addition to the existing prompt engineering and fine-tuning. However, it points out that the effect of existing latent steering is limited, and suggests standardized evaluation criteria for various actions to improve it. Based on this, we propose Instruction Attention Boosting (InstABoost), a novel latent steering technique that amplifies the effect of prompts by controlling the model's attention during the generation process. InstABoost combines the advantages of existing approaches and builds on previous studies that attention manipulation can control the compliance with contextual rules in transformer-based models. Experimental results show that InstABoost outperforms existing prompting and latent steering techniques in terms of control performance.

Takeaways, Limitations

Takeaways:
Presenting new criteria and evaluation methods for LLM production control
Proposing InstABoost technique to overcome limitations of existing potential steering
Experimentally verifying the excellent performance of InstABoost
Suggesting the possibility of LLM control through attention manipulation
Limitations:
Further research is needed on the proposed evaluation criteria and generalization performance of InstABoost.
Validation of InstABoost's applicability to various LLM architectures and sizes is needed.
Further analysis of the computational cost and efficiency of InstABoost is needed.
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