Regarding context size in LLM-based metaheuristic design

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63599958" target="_blank" >RIV/70883521:28140/25:63599958 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3712255.3734351" target="_blank" >http://dx.doi.org/10.1145/3712255.3734351</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3712255.3734351" target="_blank" >10.1145/3712255.3734351</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regarding context size in LLM-based metaheuristic design

Popis výsledku v původním jazyce

The recent and rapid progress in large language models (LLMs) has markedly influenced research efforts in the automated design and configuration of metaheuristic algorithms. A common limitation of contemporary LLMs is their finite context window, which constrains the amount of information they can effectively utilize during generation. In this study, we investigate the role of conversational context in the metaheuristic design process. This study explores two distinct aspects of LLM-based metaheuristic design: (1) the effect of conversational context on the performance of generated optimizers, and (2) its influence on the validity of generated code. Both are investigated using the EASE framework. The findings yield several unexpected insights, which are discussed in detail in the paper, offering a deeper understanding of how context affects the reliability and effectiveness of LLM-assisted algorithm generation.

Název v anglickém jazyce

Regarding context size in LLM-based metaheuristic design

Popis výsledku anglicky

The recent and rapid progress in large language models (LLMs) has markedly influenced research efforts in the automated design and configuration of metaheuristic algorithms. A common limitation of contemporary LLMs is their finite context window, which constrains the amount of information they can effectively utilize during generation. In this study, we investigate the role of conversational context in the metaheuristic design process. This study explores two distinct aspects of LLM-based metaheuristic design: (1) the effect of conversational context on the performance of generated optimizers, and (2) its influence on the validity of generated code. Both are investigated using the EASE framework. The findings yield several unexpected insights, which are discussed in detail in the paper, offering a deeper understanding of how context affects the reliability and effectiveness of LLM-assisted algorithm generation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

PROCEEDINGS OF THE 2025 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2025 COMPANION

ISBN

979-8-4007-1464-1

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

2345-2353

Název nakladatele

Association for Computing Machinery, Inc

Místo vydání

New York

Místo konání akce

Malaga

Datum konání akce

14. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001564494900376