From Black Box to Controller: Inference-Time Steering of LLM via Feature Amplification
Inference-time steering is crucial for deploying Large Language Models (LLMs) in real-world applications, where they must flexibly adjust behavior preferences.
ProofPaper ↗
Key points
- This requirement introduces a challenge: modulating the intensity of LLM preferences.
- To address this, we introduce NeuroSteer, a plug-and-play framework that enables flexible, quantitative adjustment of LLM preferences at inference time.
- NeuroSteer employs a Sparse Autoencoder (SAE) as its controller.
- Our experiments show that NeuroSteer can alter sentiment, toxicity, stance, and politeness at inference time.
Sources (1)
- [1]From Black Box to Controller: Inference-Time Steering of LLM via Feature AmplificationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:45 AM
Inference-time steering is crucial for deploying Large Language Models (LLMs) in real-world applications, where they must flexibly adjust behavior preferences.
This requirement introduces a challenge: modulating the intensity of LLM preferences.
Extractive summary: sentences quoted from the sources.
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