Summary of Context Steering: Controllable Personalization at Inference Time, by Jerry Zhi-yang He et al.
Context Steering: Controllable Personalization at Inference Time
by Jerry Zhi-Yang He, Sashrika Pandey, Mariah L. Schrum, Anca Dragan
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Context Steering (CoS), a simple decoding approach for large language models (LLMs) that amplifies the influence of context in next-token predictions. CoS computes contextual influence by comparing output probabilities from two LLM forward passes: one with and one without context. This allows practitioners to control personalization degree for different use cases. The authors demonstrate strong performance of CoS in personalized recommendations and show its potential applications as a Bayesian Generative model for inferring correlations between open-ended texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to help big language models understand the right information for each person. They want these models to be able to give better answers by knowing things like age, location, and culture. This is hard because it requires finding the right balance between giving personalized answers and being general enough for everyone. The current methods are not very flexible or easy to use. The scientists introduce a new technique called Context Steering that can make these language models more personal and adaptable to different situations. |
Keywords
» Artificial intelligence » Generative model » Token
