https://arxiv.org/abs/2412.10427

Change to arXiv's privacy policy

The arXiv Privacy Policy has changed. By continuing to use arxiv.org,
you are agreeing to the privacy policy.

I Understand
Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2412.10427
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Computation and Language

arXiv:2412.10427 (cs)
[Submitted on 10 Dec 2024]

Title:Identifying and Manipulating Personality Traits in LLMs Through
Activation Engineering

Authors:Rumi A. Allbert, James K. Wiles
View a PDF of the paper titled Identifying and Manipulating
Personality Traits in LLMs Through Activation Engineering, by Rumi A.
Allbert and James K. Wiles
View PDF HTML (experimental)

    Abstract:The field of large language models (LLMs) has grown
    rapidly in recent years, driven by the desire for better
    efficiency, interpretability, and safe use. Building on the novel
    approach of "activation engineering," this study explores
    personality modification in LLMs, drawing inspiration from
    research like Refusal in LLMs Is Mediated by a Single Direction (
    arXiv:2406.11717) and Steering Llama 2 via Contrastive Activation
    Addition (arXiv:2312.06681). We leverage activation engineering
    to develop a method for identifying and adjusting activation
    directions related to personality traits, which may allow for
    dynamic LLM personality fine-tuning. This work aims to further
    our understanding of LLM interpretability while examining the
    ethical implications of such developments.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2412.10427 [cs.CL]
          (or arXiv:2412.10427v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2412.10427
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Rumi Allbert [view email]
[v1] Tue, 10 Dec 2024 23:15:25 UTC (11,502 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Identifying and Manipulating
    Personality Traits in LLMs Through Activation Engineering, by
    Rumi A. Allbert and James K. Wiles
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

license icon view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2024-12
Change to browse by:
cs
cs.AI

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] alphaXiv Toggle
alphaXiv (What is alphaXiv?)
[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] Huggingface Toggle
Hugging Face (What is Huggingface?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
[ ] Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack