arXiv:2405.15454v4 Announce Type: replace Abstract: The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are both computationally efficient and enjoy performance guarantees. To address this need, we use a common model of concept semantics as linearly represented in an LLM's latent space. In particular, we take the view that natural language generation traces a trajectory in this continuous semantic space, realized by the language model's hidden activations. This view permits a control-theoretic treatment of
The increasing prevalence of LLMs in critical applications necessitates efficient and reliable control over their generative outputs, driving research into new methodologies.
This research outlines a novel approach to steer LLM behavior using linear semantic control, promising more predictable and performant language generation for various applications.
The ability to predictably control LLM outputs at a semantic level, moving beyond traditional prompt engineering or finetuning, offers a new paradigm for interacting with and deploying AI.
- · AI developers
- · Enterprises deploying LLMs
- · Computational linguists
- · Control systems engineers
- · Platforms with weak LLM control mechanisms
- · Developers relying solely on brute-force prompting
More reliable and less 'hallucinatory' AI applications become feasible across industries.
Reduced operational risks associated with deploying autonomous AI systems, potentially accelerating their adoption in sensitive sectors.
The development of a 'semantic API' for LLMs, allowing programmatic control over AI's creative and analytical processes.
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Read at arXiv cs.CL