---
OA_place: publisher
OA_type: green
_id: '21857'
abstract:
- lang: eng
  text: "The availability of powerful open-source large language models (LLMs) opens
    exciting use cases, such as using personal data to fine-tune these models to imitate
    a user’s unique writing style. Two key requirements for this functionality are
    personalization–in the sense that the output should recognizably reflect the user’s
    own writing style—and privacy–users may justifiably be wary of uploading extremely
    personal data, such as their email archive, to a third-party service. In this
    paper, we demonstrate the feasibility of training and running such an assistant,
    which we call Panza, on commodity hardware, for the specific use case of email
    generation. Panza’s personalization features are based on a combination of parameter-efficient
    fine-tuning using a variant of the Reverse Instructions technique [1] and Retrieval-Augmented
    Generation (RAG) [2]. We demonstrate that this combination allows us to fine-tune
    an LLM to reflect a user’s writing style using limited data, while executing on
    extremely limited resources, e.g. on a free Google Colab instance. Our key methodological
    contribution is the first detailed study of evaluation metrics for this task,
    and\r\nof how different choices of system components–the use of RAG and of different
    fine-tuning approaches–impact the system’s performance. Additionally, we demonstrate
    that very little data - under 100 email samples - are sufficient to create models
    that convincingly imitate humans, showcasing a previously unknown attack vector
    in language models. We are releasing the full Panza code as well as three new
    email datasets licensed for research use."
article_number: '81'
article_processing_charge: No
author:
- first_name: Armand
  full_name: Nicolicioiu, Armand
  last_name: Nicolicioiu
- first_name: Eugenia B
  full_name: Iofinova, Eugenia B
  id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
  last_name: Iofinova
  orcid: 0000-0002-7778-3221
- first_name: Andrej
  full_name: Jovanovic, Andrej
  last_name: Jovanovic
- first_name: Eldar
  full_name: Kurtic, Eldar
  id: 47beb3a5-07b5-11eb-9b87-b108ec578218
  last_name: Kurtic
- first_name: Mahdi
  full_name: Nikdan, Mahdi
  id: 66374281-f394-11eb-9cf6-869147deecc0
  last_name: Nikdan
- first_name: Andrei
  full_name: Panferov, Andrei
  id: 2c18daae-4dbe-11ef-8491-98ce2d960f09
  last_name: Panferov
- first_name: Ilia
  full_name: Markov, Ilia
  id: D0CF4148-C985-11E9-8066-0BDEE5697425
  last_name: Markov
- first_name: Nir
  full_name: Shavit, Nir
  last_name: Shavit
- first_name: Dan-Adrian
  full_name: Alistarh, Dan-Adrian
  id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
  last_name: Alistarh
  orcid: 0000-0003-3650-940X
citation:
  ama: 'Nicolicioiu A, Iofinova EB, Jovanovic A, et al. <i>Panza: Investigating the
    Feasibility of Fully-Local Personalized Text Generation</i>. OpenReview; 2026.'
  apa: 'Nicolicioiu, A., Iofinova, E. B., Jovanovic, A., Kurtic, E., Nikdan, M., Panferov,
    A., … Alistarh, D.-A. (2026). <i>Panza: Investigating the feasibility of fully-local
    personalized text generation</i>. <i>Third Conference on Parsimony and Learning
    (Proceedings Track)</i>. Tübíngen, Germany: OpenReview.'
  chicago: 'Nicolicioiu, Armand, Eugenia B Iofinova, Andrej Jovanovic, Eldar Kurtic,
    Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, and Dan-Adrian Alistarh.
    <i>Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation</i>.
    <i>Third Conference on Parsimony and Learning (Proceedings Track)</i>. OpenReview,
    2026.'
  ieee: 'A. Nicolicioiu <i>et al.</i>, <i>Panza: Investigating the feasibility of
    fully-local personalized text generation</i>. OpenReview, 2026.'
  ista: 'Nicolicioiu A, Iofinova EB, Jovanovic A, Kurtic E, Nikdan M, Panferov A,
    Markov I, Shavit N, Alistarh D-A. 2026. Panza: Investigating the feasibility of
    fully-local personalized text generation, OpenReview,p.'
  mla: 'Nicolicioiu, Armand, et al. “Panza: Investigating the Feasibility of Fully-Local
    Personalized Text Generation.” <i>Third Conference on Parsimony and Learning (Proceedings
    Track)</i>, 81, OpenReview, 2026.'
  short: 'A. Nicolicioiu, E.B. Iofinova, A. Jovanovic, E. Kurtic, M. Nikdan, A. Panferov,
    I. Markov, N. Shavit, D.-A. Alistarh, Panza: Investigating the Feasibility of
    Fully-Local Personalized Text Generation, OpenReview, 2026.'
conference:
  end_date: 2026-03-26
  location: Tübíngen, Germany
  name: 'CPAL: Conference on Parsimony and Learning'
  start_date: 2026-03-23
corr_author: '1'
date_created: 2026-05-11T08:50:28Z
date_published: 2026-03-06T00:00:00Z
date_updated: 2026-07-27T12:50:03Z
day: '06'
department:
- _id: GradSch
- _id: DaAl
keyword:
- LLMs
- PEFT
- LoRA
- personalization
- efficient ML
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://openreview.net/pdf?id=soFWnTqd23
month: '03'
oa: 1
oa_version: Accepted Version
publication: Third Conference on Parsimony and Learning (Proceedings Track)
publication_status: published
publisher: OpenReview
quality_controlled: '1'
related_material:
  record:
  - id: '21854'
    relation: dissertation_contains
    status: public
status: public
title: 'Panza: Investigating the feasibility of fully-local personalized text generation'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference_poster
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2026'
...