Summary of Panza: Design and Analysis Of a Fully-local Personalized Text Writing Assistant, by Armand Nicolicioiu et al.
Panza: Design and Analysis of a Fully-Local Personalized Text Writing Assistant
by Armand Nicolicioiu, Eugenia Iofinova, Andrej Jovanovic, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, Dan Alistarh
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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 A medium-difficulty summary of the abstract: This paper presents a new design and evaluation for an automated email generation assistant called Panza, which can fine-tune large language models (LLMs) to imitate a user’s unique writing style. The key requirements for such assistants are personalization, recognizing the user’s own writing style, and privacy, ensuring that users’ personal data is not compromised. To achieve this, the authors combine fine-tuning using Reverse Instructions with Retrieval-Augmented Generation (RAG). They demonstrate that this combination allows them to fine-tune an LLM to reflect a user’s writing style using limited data, while executing on extremely limited resources, such as a free Google Colab instance. The paper also provides a detailed study of evaluation metrics for this personalized writing task and how different system components impact the system’s performance. Furthermore, they show that very little data (under 100 email samples) are sufficient to create models that convincingly imitate humans. This finding highlights a previously unknown attack vector in language models – that access to a small number of writing samples can allow an attacker to cheaply create generative models that imitate a target’s writing style. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This paper is about creating a new kind of computer program that helps people write emails by automatically generating text in their own style. The program, called Panza, needs to be personalized to recognize the user’s writing style and private to protect sensitive information. To achieve this, the authors use special techniques to fine-tune large language models. They show that these models can be trained using very little data (less than 100 emails) and still produce realistic results. This has important implications for how we use language models in the future. |
Keywords
* Artificial intelligence * Fine tuning * Rag * Retrieval augmented generation
