Summary of Few-shot Personalization Of Llms with Mis-aligned Responses, by Jaehyung Kim et al.
Few-shot Personalization of LLMs with Mis-aligned Responses
by Jaehyung Kim, Yiming Yang
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 a novel approach for personalizing large language models (LLMs) by learning customized prompts for each user based on their profile and a few examples of previous opinions. The authors develop an iterative process that incorporates the contexts of mis-aligned responses from LLMs to improve prompt effectiveness. They also introduce an inference method leveraging test query context and personalized prompts. Experimental results show significant performance improvements across various benchmarks compared to best-performing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models give better answers by making them more personal. Right now, these models don’t do a great job of understanding individual people’s thoughts and opinions. The authors suggest a new way to improve this by creating personalized prompts for each person based on some basic information about them and a few examples of what they like or dislike. They also develop a way to use the context of an answer that doesn’t quite match what was asked, which is important for making answers more personal. The results show that their approach works really well. |
Keywords
* Artificial intelligence * Inference * Prompt
