SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models

• URL: http://arxiv.org/abs/2502.11356v1
• Date: Mon, 17 Feb 2025 02:11:17 GMT
• Title: SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models
• Authors: Zirui He, Haiyan Zhao, Yiran Qiao, Fan Yang, Ali Payani, Jing Ma, Mengnan Du,
• Abstract summary: This paper presents a novel framework that leverages sparse autoencoders (SAE) to interpret how instruction following works in large language models. We demonstrate how the features we identify can effectively steer model outputs to align with given instructions. Our findings reveal that instruction following capabilities are encoded by a distinct set of instruction-relevant SAE latents.
• Score: 21.272449543430078
• License:
• Abstract: The ability of large language models (LLMs) to follow instructions is crucial for their practical applications, yet the underlying mechanisms remain poorly understood. This paper presents a novel framework that leverages sparse autoencoders (SAE) to interpret how instruction following works in these models. We demonstrate how the features we identify can effectively steer model outputs to align with given instructions. Through analysis of SAE latent activations, we identify specific latents responsible for instruction following behavior. Our findings reveal that instruction following capabilities are encoded by a distinct set of instruction-relevant SAE latents. These latents both show semantic proximity to relevant instructions and demonstrate causal effects on model behavior. Our research highlights several crucial factors for achieving effective steering performance: precise feature identification, the role of final layer, and optimal instruction positioning. Additionally, we demonstrate that our methodology scales effectively across SAEs and LLMs of varying sizes.

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