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Computer Science > Computation and Language

arXiv:2404.04671 (cs)
[Submitted on 6 Apr 2024 (v1), last revised 16 Jun 2024 (this
version, v3)]

Title:PhyloLM : Inferring the Phylogeny of Large Language Models and
Predicting their Performances in Benchmarks

Authors:Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri
View a PDF of the paper titled PhyloLM : Inferring the Phylogeny of
Large Language Models and Predicting their Performances in
Benchmarks, by Nicolas Yax and 2 other authors
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    Abstract:This paper introduces PhyloLM, a method adapting
    phylogenetic algorithms to Large Language Models (LLMs) to
    explore whether and how they relate to each other and to predict
    their performance characteristics. Our method calculates a
    phylogenetic distance metrics based on the similarity of LLMs'
    output. The resulting metric is then used to construct
    dendrograms, which satisfactorily capture known relationships
    across a set of 111 open-source and 45 closed models.
    Furthermore, our phylogenetic distance predicts performance in
    standard benchmarks, thus demonstrating its functional validity
    and paving the way for a time and cost-effective estimation of
    LLM capabilities. To sum up, by translating population genetic
    concepts to machine learning, we propose and validate a tool to
    evaluate LLM development, relationships and capabilities, even in
    the absence of transparent training information.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG);
          Populations and Evolution (q-bio.PE)
Cite as:  arXiv:2404.04671 [cs.CL]
          (or arXiv:2404.04671v3 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2404.04671
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Nicolas Yax [view email]
[v1] Sat, 6 Apr 2024 16:16:30 UTC (4,036 KB)
[v2] Thu, 23 May 2024 16:03:29 UTC (11,764 KB)
[v3] Sun, 16 Jun 2024 14:39:20 UTC (11,764 KB)
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