https://arxiv.org/abs/2406.02543

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2406.02543
[                    ]

Help | Advanced Search

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

  * Login
  * Help Pages
  * About

Computer Science > Machine Learning

arXiv:2406.02543 (cs)
[Submitted on 4 Jun 2024]

Title:To Believe or Not to Believe Your LLM

Authors:Yasin Abbasi Yadkori, Ilja Kuzborskij, Andras Gyorgy, Csaba
Szepesvari
View a PDF of the paper titled To Believe or Not to Believe Your LLM,
by Yasin Abbasi Yadkori and 3 other authors
View PDF HTML (experimental)

    Abstract:We explore uncertainty quantification in large language
    models (LLMs), with the goal to identify when uncertainty in
    responses given a query is large. We simultaneously consider both
    epistemic and aleatoric uncertainties, where the former comes
    from the lack of knowledge about the ground truth (such as about
    facts or the language), and the latter comes from irreducible
    randomness (such as multiple possible answers). In particular, we
    derive an information-theoretic metric that allows to reliably
    detect when only epistemic uncertainty is large, in which case
    the output of the model is unreliable. This condition can be
    computed based solely on the output of the model obtained simply
    by some special iterative prompting based on the previous
    responses. Such quantification, for instance, allows to detect
    hallucinations (cases when epistemic uncertainty is high) in both
    single- and multi-answer responses. This is in contrast to many
    standard uncertainty quantification strategies (such as
    thresholding the log-likelihood of a response) where
    hallucinations in the multi-answer case cannot be detected. We
    conduct a series of experiments which demonstrate the advantage
    of our formulation. Further, our investigations shed some light
    on how the probabilities assigned to a given output by an LLM can
    be amplified by iterative prompting, which might be of
    independent interest.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
          Computation and Language (cs.CL)
Cite as:  arXiv:2406.02543 [cs.LG]
          (or arXiv:2406.02543v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2406.02543
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yasin Abbasi Yadkori [view email]
[v1] Tue, 4 Jun 2024 17:58:18 UTC (660 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled To Believe or Not to Believe Your
    LLM, by Yasin Abbasi Yadkori and 3 other authors
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

license icon view license
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2024-06
Change to browse by:
cs
cs.AI
cs.CL

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?)
[ ] 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

[ ] 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?)
[ ] 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?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)
[ ] IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)

  * 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