https://arxiv.org/abs/2502.05253

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2502.05253
[                    ]

Help | Advanced Search

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

  * Login
  * Help Pages
  * About

Computer Science > Computation and Language

arXiv:2502.05253 (cs)
[Submitted on 7 Feb 2025]

Title:LLMs Can Teach Themselves to Better Predict the Future

Authors:Benjamin Turtel, Danny Franklin, Philipp Schoenegger
View a PDF of the paper titled LLMs Can Teach Themselves to Better
Predict the Future, by Benjamin Turtel and 2 other authors
View PDF

    Abstract:We present an outcome-driven fine-tuning framework that
    enhances the forecasting capabilities of large language models
    (LLMs) without relying on human-curated reasoning samples. Our
    method leverages model self-play to generate pairs of diverse
    reasoning trajectories and probabilistic forecasts for a set of
    diverse questions that resolve after the models' knowledge cutoff
    date. We then rank pairs of these reasoning traces by their
    distance to the actual outcomes before fine-tuning the model via
    Direct Preference Optimization (DPO). On a separate test set, our
    approach increases prediction accuracy of Phi-4 14B and
    DeepSeek-R1 14B by between 7--10\% over a base model and a DPO
    fine-tuned control model with randomized labels, bringing them on
    par with forecasting capabilities of much larger frontier models
    like GPT-4o.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2502.05253 [cs.CL]
          (or arXiv:2502.05253v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2502.05253
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Philipp Schoenegger [view email]
[v1] Fri, 7 Feb 2025 17:21:16 UTC (151 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled LLMs Can Teach Themselves to
    Better Predict the Future, by Benjamin Turtel and 2 other authors
  * View PDF
  * TeX Source
  * Other Formats

view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2025-02
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