Summary of Inference Time Llm Alignment in Single and Multidomain Preference Spectrum, by Sadat Shahriar et al.
Inference time LLM alignment in single and multidomain preference spectrum
by Sadat Shahriar, Zheng Qi, Nikolaos Pappas, Srikanth Doss, Monica Sunkara, Kishaloy Halder, Manuel Mager, Yassine Benajiba
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces a novel inference-time model alignment method that learns encoded representations of preference dimensions, called Alignment Vectors (AV). The AV framework enables dynamically adjusting the Large Language Model’s behavior during inference through simple linear operations. The authors demonstrate the practical potential of this approach by applying it to three specialized domains: medical, legal, and financial, exemplifying gradual response levels across these domains. In contrast to existing methods that require full re-training or access to the reward model at each inference step, the proposed method reduces inference cost by half compared to prompt engineering approaches. The authors also show that AVs are transferable across different fine-tuning stages of the same model and facilitate multidomain, diverse preference alignment, making it 12x faster than the retraining approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand people’s preferences by creating a new way to adjust language models during use. The current methods for doing this require a lot of time and resources, or need access to special information at each step. This new method is faster and more flexible, allowing users to control the computer’s responses in different areas like medicine, law, and finance. The approach reduces costs by half compared to other methods and can be applied across multiple domains. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Inference » Large language model » Prompt
