İSTEM MÜHENDİSLİĞİNİN BÜYÜK DİL MODELLERİNİN PERFORMANSINDAKİ ROLÜ: ANALİZ VE UYGULAMA ÖRNEKLERİ

Fatma Gülşah Tan; Asım Sinan Yüksel; Muhammed Abdulhamid Karabıyık

doi:10.17780/ksujes.1480838

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İSTEM MÜHENDİSLİĞİNİN BÜYÜK DİL MODELLERİNİN PERFORMANSINDAKİ ROLÜ: ANALİZ VE UYGULAMA ÖRNEKLERİ

Öz

İstem mühendisliği, büyük dil modellerinin yeteneklerini artırmak için kritik bir teknik olarak ortaya çıkmıştır. İstem adı verilen talimatlar ile model parametrelerini değiştirmeden ince ayar yapma imkânı sunarak, bu modellerin çeşitli görevlerde üstün performans göstermesini sağlar. Bu çalışmanın temel amacı, büyük dil modeli performansını iyileştirmek, hesaplama maliyetlerini azaltmak ve kullanıcı deneyimini geliştirmek için istem mühendisliğinin nasıl etkili kullanılacağını göstermektir. Çalışmada, farklı uygulama alanlarına göre kategorize edilen ve son teknoloji 15 istem mühendisliği tekniği analiz edilmiştir. Bu teknikler, sıfır atış ve birkaç atış istemlerinden düşünce zinciri ve otomatik düşünce zinciri istemlerine kadar çeşitlilik göstermektedir. Her bir tekniğin avantajları ve dezavantajları detaylı olarak değerlendirilmiş ve performans artışının nasıl sağlandığı örnek senaryolarla gösterilmiştir. Araştırmanın sonuçları, istem mühendisliğinin büyük dil modellerinin çeşitli görev ve uygulamalarda performansını artırmada önemli bir rol oynadığını göstermektedir. Özellikle az verili öğrenme senaryolarında verimliliği artırmak ve önyargı, tutarsızlık gibi zorlukları azaltmak için yenilikçi istem mühendisliği tekniklerinin başarılı performans sergiledikleri görülmüştür. Bu bulgular, araştırmacılar ve uygulayıcılar için yol gösterici bir kaynak olarak hizmet edecek ve büyük dil modellerinin geniş çapta uygulanabilirliğini artıracaktır. Çalışmamız, istem mühendisliğinin daha iyi anlaşılmasına katkı sağlayacak ve gelecekteki araştırmalara ışık tutacaktır.

Anahtar Kelimeler

THE ROLE OF PROMPT ENGINEERING IN THE PERFORMANCE OF LARGE LANGUAGE MODELS: ANALYSIS AND APPLICATION EXAMPLES

Abstract

Prompt engineering has emerged as a critical technique for increasing the capabilities of large language models. It enables these models to perform superiorly in various tasks by providing the opportunity to fine-tune the model without changing the parameters through instructions called prompts. The main goal of this work is to show how to effectively use prompt engineering to improve large language model performance, reduce computational costs, and improve user experience. In the study, 15 state-of-the-art prompt engineering techniques, categorized according to different application areas, were analyzed. These techniques range from zero-shot and few-shot prompts to chain-of-thought and automatic-chain-of-thought prompts. The advantages and disadvantages of each technique are evaluated in detail and how the performance increase is achieved is shown with example scenarios. The results of the research show that prompt engineering plays an important role in improving the performance of large language models in a variety of tasks and applications. Innovative prompt engineering techniques have been shown to perform successfully to increase efficiency and reduce difficulties such as bias and inconsistency, especially in low-data learning scenarios. These findings will serve as a guiding resource for researchers and practitioners and will increase the broad applicability of large language models. Our study will contribute to a better understanding of prompt engineering and shed light on future research.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Yapay Zeka (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Fatma Gülşah Tan ^*
0000-0002-2748-0396
Türkiye

Asım Sinan Yüksel
0000-0003-1986-5269
Türkiye

Muhammed Abdulhamid Karabıyık
0000-0001-7927-8790
Türkiye

Yayımlanma Tarihi

3 Aralık 2024

Gönderilme Tarihi

8 Mayıs 2024

Kabul Tarihi

25 Haziran 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1480838

IZ

https://izlik.org/JA86DH24EZ

Kaynak Göster

RIS / Bibtex

APA

Tan, F. G., Yüksel, A. S., & Karabıyık, M. A. (2024). İSTEM MÜHENDİSLİĞİNİN BÜYÜK DİL MODELLERİNİN PERFORMANSINDAKİ ROLÜ: ANALİZ VE UYGULAMA ÖRNEKLERİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 1401-1420. https://doi.org/10.17780/ksujes.1480838

Cited By

The development and evaluation of agricultural question-answering systems based on large language models

Scientific Reports

https://doi.org/10.1038/s41598-026-35003-9