Mixture of Tunable Experts - Behavior Modification of DeepSeek-R1 at Inference Time

• URL: http://arxiv.org/abs/2502.11096v1
• Date: Sun, 16 Feb 2025 12:24:39 GMT
• Title: Mixture of Tunable Experts - Behavior Modification of DeepSeek-R1 at Inference Time
• Authors: Robert Dahlke, Henrik Klagges, Dan Zecha, Benjamin Merkel, Sven Rohr, Fabian Klemm,
• Abstract summary: We present a method that extends the Mixture-of-Experts architecture of Large Language Models (LLMs) MoTE enables meaningful and focused behavior changes in LLMs on-the-fly during inference time.
• Score: 1.1655046053160683
• License:
• Abstract: We present the Mixture-of-Tunable-Experts (MoTE), a method that extends the Mixture-of-Experts architecture of Large Language Models (LLMs). Without additional training, MoTE enables meaningful and focused behavior changes in LLMs on-the-fly during inference time. By analyzing the digital LLM brain of DeepSeek-R1 using a technique we dub 'functional Token Resonance Imaging' (fTRI) - inspired by fMRI and using prompts designed to elicit specific behavior (e.g., 'What happened {time}{place}?') - we empirically identify distinctive experts associated with behaviors like refusal responses. Using MoTE we are able to intervene and control such specific behavior. We switched off the top 10 most refusal-relevant experts (0.07% of R1's 14,848 routed experts), achieving a 52% refusal reduction on sensitive reference prompts without performance degradation on MT-Bench. Random expert deactivation resulted in smaller behavioral shifts with increased noise, whereas forced expert activation led to significantly higher refusal rates. Our approach shares similarities with sparse autoencoders (SAEs) in terms of explainability and steerability. Unlike SAEs, MoTE does not require large training efforts, as within MoEs with a vast number of experts, specialization already emerged naturally during pretraining. Our findings suggest that significant functional mechanisms in Mixture-of-Experts architectures can at least partially be localized in a small number of specific experts, rather than being distributed throughout the model's weights. Expert subgroups can be tuned to trigger significant behavior variations, providing insights into the inner workings of LLMs.

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