Optimove

optimove

Customer segmentation is a necessary part of the marketing strategy for any industry. Demographics like age, gender, and location provide easy wins for companies looking to personalize their marketing materials. In the next few years, the tools that we use for will give every company a greater understanding of every customer on an individual level. These tools will quickly become the norm for any digital business looking to improve their data analysis. segmentation Still, there are some common misunderstandings about the practice on the whole. Before diving in head-first, consider these five factors: Customer behavior is just as important as customer details Effective segmentation digs deeper. It utilizes customer behavior, not just your run-of-the-mill data. What actions are customers taking once they hit your website? Do their actions resemble those of other customers? Does there seem to be a trend? We do see segmentation by customer actions, but few brands dive into it as deeply as they should. Many companies sort customers based on who abandons their cart on e-commerce sites. In these cases, companies might offer a discount or reach out to ask if they had any questions about the product. But, what if you segmented that group even further? Further segments could include those for customers who have never entered their credit card, customers whose credit card has been denied, or customers who have never entered a single detail after adding a product to their cart. By tracking and sorting customers based on their behavior on your site, you can better inform your marketing materials and custom-craft your messages for each customer type. You can then design your landing pages to target specific customer types. Many landing page builders, such as , let you incorporate segmentation into your landing pages and other marketing materials. Unbounce Automation and machine learning are inherent parts of effective segmentation A big reason so few brands haven’t used segmentation to its full potential is because sorting through all that data can be tedious. It can take days to sift through data by hand and properly categorize each person to ensure your assessments are accurate. And accuracy is important here: you wouldn’t want to send out customer emails only to find that you have miscalculated or missed a data point. Automation and machine learning have re-shaped digital marketing and segmentation in particular. Tools like provide hyper-targeting that examines the customer journey and then automatically optimizes your marketing materials for specific customer types, helping you interact with customers on a more personal level. Optimove’s software Micro-segmentation builds relationships Customers love brands that understand them. That’s why it’s so important to speak to their pain points in every piece of marketing that we create. Customers want to know that companies understand their needs, pains, and desires. They also want to be assured that the product you offer will solve their relevant problems. Micro-segmentation is about sorting your customers into more specific categories. In typical segmentation, you might have customer segments based on who lives in Denver, who has a job title of Vice President, or who is above the age of 50. An example of a micro-segment would be a segment that includes all 3–50+ year old VPs who live in Denver. According to an , 78% of customers stated that they’re more likely to buy from a company that sends them more targeted offers. Building that initial trust is incredibly important — customers who have been buying from a company for 30 or more months than they did on their first purchase. Infosys survey spend 67% more per order Micro-segmentation gives you the opportunity to speak to their biggest concerns and win their trust by helping them to address them. Segment research should inform product development At its core, segmentation begins with learning more about your customers. The more you know about your customers, the more you can tailor your marketing to their unique problems, preferences, and desires. Startups often to find a market that needs solutions. But once a company is sufficiently settled within their market and earning a healthy profit, they often shift their focus to other areas of their business. pivot While no established business is going to do a full-fledged pivot in the same way that a startup would, many could benefit from more customer data involvement in product development. Segmentation data should go beyond marketing; it should also be used to inform product development. Great customer data is the most powerful tool that companies have available to them, but too many continue to operate based on perception rather than data-backed facts. Customer journeys and segments are not static Another big mistake that brands make when segmenting is that once a customer has been placed in a segment, their analysis stops there. These customers will forever remain in that segment — even if additional data is collected in the future. Customer segments can and should change over time. Today’s customers may be in a completely different place in life than they had been a year prior. Continually working to correctly categorize your customer types will make all aspects of your marketing more effective. Personalization is the future Deep segmentation of your audience gives you more opportunities for personalization. It allows you to gain a deeper analysis of each customer and their specific situation and to tailor your sales and marketing efforts specifically to them. While segmentation itself isn’t a new concept in digital marketing, the tools that we have available have allowed for micro-segmentation to become affordable for companies of all sizes.

The 5 Factors to Consider When Segmenting Your Customers

CARY, NC, October 28th, 2024/CyberNewsWire/--As the year-end approaches, it’s common for enterprises to discover they still have funds that must be utilized. Often, these L&D dollars are "use or lose," meaning they will be returned to the general fund if not invested. Recognizing this, INE Security is launching an initiative to guide organizations in investing in technical training before the year end. This approach not only ensures wise expenditure of remaining budgets but also significantly strengthens organizational security postures. Addressing Training Budgets: Year-End Budget Scenario: It's common for organizations to approach year-end with an unused budget designated for training. If not spent, these funds often return to general accounts or are lost altogether, missing an opportunity for strategic investment. Strategic Spending: INE Security encourages using these funds to invest in team cybersecurity training, turning what could be wasted resources into a pivotal investment in security and professional development. Advantages of Utilizing Surplus Funds: Proactively using surplus training budgets can help organizations make strategic decisions that align with long-term goals, improving security protocols and fostering a knowledgeable workforce. How Organizations Can Utilize Available Training Budgets: Skill Enhancement: Upgrade the team's skills to defend against the latest networking, cloud, and cybersecurity threats. Employee Retention: Invest in employees' growth to boost morale and retention - especially in a tight job market where your best employees may be lured to other companies. Future-Proofing: Prepare teams for future challenges with forward-looking training programs. Compliance and Standards: Ensure compliance with industry standards and regulations through accredited courses and certifications - bolstering your team for contract awards and impending compliance requirements (CMMC). “In a time when digital threats are increasing in both complexity and frequency, proactive Networking, Cloud, and Cybersecurity training is more crucial than ever,” said Dara Warn, CEO of INE Security. “We recognize that many organizations end the year with a surplus in their training budgets. This is an excellent opportunity to invest in vital training, ensuring teams are not only prepared but are ahead of the curve.” Organizations can benefit by engaging in INE Security’s expert-led courses and hands-on labs, which are designed to provide real-world experience and in-depth knowledge across Networking, Cloud, and Cybersecurity. In recognition of the often-underused training budgets available at year's end, INE Security is offering significant discounts for team training on two-year deals. This initiative not only helps organizations optimize their unspent training funds but also boosts their long-term security strategies. To learn more about INE’s comprehensive Networking, Cloud, and Cybersecurity training programs, users can visit www.ine.com or contact INE Security’s corporate training advisors directly. About INE INE Security is the premier provider of online technical training for the IT/IS industry. Harnessing a powerful hands-on lab platform, cutting-edge technology, a global video distribution network, and world-class instructors, INE Security is the top training choice for Fortune 500 companies worldwide and for IT/IS professionals looking to advance their careers. INE’s suite of learning paths offers an incomparable depth of expertise across cybersecurity, cloud, networking, and data science. INE Security is committed to delivering advanced technical training while also lowering the barriers worldwide for those looking to enter and excel in a cybersecurity career. Contact Director of Global Strategic Communications and Events Kathryn Brown INE Security kbrown@ine.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here CARY, NC, October 28th, 2024/CyberNewsWire/--As the year-end approaches, it’s common for enterprises to discover they still have funds that must be utilized. Often, these L&D dollars are "use or lose," meaning they will be returned to the general fund if not invested. Recognizing this, INE Security is launching an initiative to guide organizations in investing in technical training before the year end. This approach not only ensures wise expenditure of remaining budgets but also significantly strengthens organizational security postures. Addressing Training Budgets: Year-End Budget Scenario: It's common for organizations to approach year-end with an unused budget designated for training. If not spent, these funds often return to general accounts or are lost altogether, missing an opportunity for strategic investment. Strategic Spending: INE Security encourages using these funds to invest in team cybersecurity training, turning what could be wasted resources into a pivotal investment in security and professional development. Advantages of Utilizing Surplus Funds: Proactively using surplus training budgets can help organizations make strategic decisions that align with long-term goals, improving security protocols and fostering a knowledgeable workforce. How Organizations Can Utilize Available Training Budgets: Skill Enhancement: Upgrade the team's skills to defend against the latest networking, cloud, and cybersecurity threats. Employee Retention: Invest in employees' growth to boost morale and retention - especially in a tight job market where your best employees may be lured to other companies. Future-Proofing: Prepare teams for future challenges with forward-looking training programs. Compliance and Standards: Ensure compliance with industry standards and regulations through accredited courses and certifications - bolstering your team for contract awards and impending compliance requirements (CMMC). “In a time when digital threats are increasing in both complexity and frequency, proactive Networking, Cloud, and Cybersecurity training is more crucial than ever,” said Dara Warn, CEO of INE Security. “In a time when digital threats are increasing in both complexity and frequency, proactive Networking, Cloud, and Cybersecurity training is more crucial than ever,” said Dara Warn, CEO of INE Security. “We recognize that many organizations end the year with a surplus in their training budgets. This is an excellent opportunity to invest in vital training, ensuring teams are not only prepared but are ahead of the curve.” “We recognize that many organizations end the year with a surplus in their training budgets. This is an excellent opportunity to invest in vital training, ensuring teams are not only prepared but are ahead of the curve.” Organizations can benefit by engaging in INE Security’s expert-led courses and hands-on labs, which are designed to provide real-world experience and in-depth knowledge across Networking, Cloud, and Cybersecurity. In recognition of the often-underused training budgets available at year's end, INE Security is offering significant discounts for team training on two-year deals. This initiative not only helps organizations optimize their unspent training funds but also boosts their long-term security strategies. To learn more about INE’s comprehensive Networking, Cloud, and Cybersecurity training programs, users can visit www.ine.com or contact INE Security’s corporate training advisors directly. About INE INE Security is the premier provider of online technical training for the IT/IS industry. Harnessing a powerful hands-on lab platform, cutting-edge technology, a global video distribution network, and world-class instructors, INE Security is the top training choice for Fortune 500 companies worldwide and for IT/IS professionals looking to advance their careers. INE’s suite of learning paths offers an incomparable depth of expertise across cybersecurity, cloud, networking, and data science. INE Security is committed to delivering advanced technical training while also lowering the barriers worldwide for those looking to enter and excel in a cybersecurity career. Contact Director of Global Strategic Communications and Events Kathryn Brown INE Security kbrown@ine.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here

INE's Initiative To Optimize Year-End Training Budgets With Enhanced Cybersecurity And Networking 

مبادرة INE لتحسين ميزانيات التدريب في نهاية العام من خلال تحسين الأمن السيبراني ومهارات الشبكات

INE-nin Təkmil Kibertəhlükəsizlik və Şəbəkə ilə İl Sonu Təlim Büdcələrini Optimallaşdırmaq Təşəbbüsü

La iniciativa del INE para optimizar los presupuestos de formación de fin de año con mejoras en ciberseguridad y redes

ابتکار INE برای بهینه‌سازی بودجه‌های آموزشی پایان سال با امنیت سایبری پیشرفته و روابط عمومی شبکه

Iniciativa do INE para optimizar os orzamentos de formación de fin de ano cunha mellora da ciberseguridade e a rede Pr

INE のサイバーセキュリティとネットワーク強化による年末トレーニング予算の最適化の取り組み

ຂໍ້ລິເລີ່ມຂອງ INE ເພື່ອເພີ່ມປະສິດທິພາບງົບປະມານການຝຶກອົບຮົມທ້າຍປີດ້ວຍການເພີ່ມປະສິດທິພາບ Cybersecurity ແລະເຄືອຂ່າຍ Pr

Иницијативата на INE за оптимизирање на буџетите за обука до крајот на годината со зајакната сајбер безбедност и вмрежување Pr

INE को पहल परिष्कृत साइबरसुरक्षा र नेटवर्किङ Pr संग वर्ष-अन्त प्रशिक्षण बजेटहरू अनुकूलन गर्न

Iniciativa do INE para otimizar os orçamentos de treinamento de fim de ano com segurança cibernética aprimorada e rede Pr

Ташаббуси INE барои оптимизатсия кардани буҷетҳои омӯзишӣ дар охири сол бо мукаммали киберамният ва шабакавӣ

Ініціатива INE щодо оптимізації бюджетів на навчання на кінець року за допомогою покращеної кібербезпеки та мережевих технологій

Pretty Slow / Ugly Fast: How to Optimize your Code in 19 Different Ways

5 Steps to Build an Exchange-Optimized L3 Rollup "Eventum"

5 pasos para crear un paquete acumulativo L3 optimizado para Exchange "Eventum"

5 koraka za izgradnju L3 skupnog paketa "Eventum" optimiziranog za Exchange

5 שלבים לבניית אוסף L3 המותאם ל-Exchange "Eventum"

Exchange に最適化された L3 ロールアップ「Eventum」を構築するための 5 つの手順

5 étapes ya kotonga "Eventum" ya Rollup L3 optimisée na échange.

5 ຂັ້ນຕອນເພື່ອສ້າງການແລກປ່ຽນແບບ L3 Rollup "Eventum" ທີ່ດີທີ່ສຸດ.

5 krokov na vybudovanie súhrnu L3 „Eventum“ optimalizovaného pre výmenu

Hatua 5 za Kuunda "Tukio" la L3 lililoboreshwa na Ubadilishanaji

5 Қадам барои сохтани як маҷмӯаи барои мубодилаи оптимизатсияшудаи L3 "Eventum"

Bir Exchangea-optimizirlenen L3 Rollup "Waka" gurmak üçin 5 ädim

Borsaya Optimize Edilmiş L3 Rollup "Eventum" Oluşturmak İçin 5 Adım

5 кроків для створення оптимізованого для Exchange зведеного пакета L3 "Eventum"

How to Optimize Kubernetes for Large Docker Images

Cómo optimizar Kubernetes para imágenes Docker de gran tamaño

כיצד לייעל את Kubernetes עבור תמונות Docker גדולות

Ki jan yo Optimize Kubernetes pou gwo imaj Docker

A Kubernetes optimalizálása a nagy Docker-képekhez

大規模な Docker イメージ向けに Kubernetes を最適化する方法

Ahoana ny fomba hanatsarana ny Kubernetes ho an'ny sary Docker lehibe

Ako optimalizovať Kubernetes pre veľké obrázky Docker

Si të optimizoni Kubernetes për imazhe të mëdha Docker

วิธีเพิ่มประสิทธิภาพ Kubernetes สำหรับภาพ Docker ขนาดใหญ่

Як оптимізувати Kubernetes для великих зображень Docker

Uyenza njani iKubernetes kwiMifanekiso yeDocker enkulu

Authors:
(1) Chengrun Yang, Google DeepMind and Equal contribution;
(2) Xuezhi Wang, Google DeepMind;
(3) Yifeng Lu, Google DeepMind;
(4) Hanxiao Liu, Google DeepMind;
(5) Quoc V. Le, Google DeepMind;
(6) Denny Zhou, Google DeepMind;
(7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 7 Conclusion, Acknowledgments and References A Some Failure Cases B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions C.2 META-PROMPT FOR PROMPT OPTIMIZATION Different optimizer models work the best on different styles of meta-prompts. Figure 3 in the main paper shows the meta-prompt for PaLM 2-L-IT; Figure 21 shows that for pre-trained PaLM 2-L; Figure 22 shows that for GPT models. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Authors: Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.2 Main Results 5.3 Ablation Studies 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 6 Related Work 7 Conclusion, Acknowledgments and References 7 Conclusion, Acknowledgments and References A Some Failure Cases A Some Failure Cases B Prompting Formats for Scorer Llm B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions C.2 META-PROMPT FOR PROMPT OPTIMIZATION C.2 META-PROMPT FOR PROMPT OPTIMIZATION Different optimizer models work the best on different styles of meta-prompts. Figure 3 in the main paper shows the meta-prompt for PaLM 2-L-IT; Figure 21 shows that for pre-trained PaLM 2-L; Figure 22 shows that for GPT models. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Large Language Models as Optimizers: Meta-Prompt for Prompt Optimization

Authors:
(1) Chengrun Yang, Google DeepMind and Equal contribution;
(2) Xuezhi Wang, Google DeepMind;
(3) Yifeng Lu, Google DeepMind;
(4) Hanxiao Liu, Google DeepMind;
(5) Quoc V. Le, Google DeepMind;
(6) Denny Zhou, Google DeepMind;
(7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 7 Conclusion, Acknowledgments and References A Some Failure Cases B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions C META-PROMPTS C.1 META-PROMPT FOR MATH OPTIMIZATION This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Authors: Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.2 Main Results 5.3 Ablation Studies 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 6 Related Work 7 Conclusion, Acknowledgments and References 7 Conclusion, Acknowledgments and References A Some Failure Cases A Some Failure Cases B Prompting Formats for Scorer Llm B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions C META-PROMPTS C.1 META-PROMPT FOR MATH OPTIMIZATION This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Large Language Models as Optimizers: Meta-Prompt for Math Optimization

Authors:
(1) Chengrun Yang, Google DeepMind and Equal contribution;
(2) Xuezhi Wang, Google DeepMind;
(3) Yifeng Lu, Google DeepMind;
(4) Hanxiao Liu, Google DeepMind;
(5) Quoc V. Le, Google DeepMind;
(6) Denny Zhou, Google DeepMind;
(7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 7 Conclusion, Acknowledgments and References A Some Failure Cases B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions 2 OPRO: LLM AS THE OPTIMIZER Figure 2 illustrates the overall framework of OPRO. In each optimization step, the LLM generates candidate solutions to the optimization task based on the optimization problem description and previously evaluated solutions in the meta-prompt. Then the new solutions are evaluated and added to the meta-prompt for the subsequent optimization process. The optimization process terminates when the LLM is unable to propose new solutions with better optimization scores, or a maximum number of optimization steps has reached. We first outline the desired features of LLMs for optimization, then describe the key design choices based on these desirables. 2.1 DESIRABLES OF OPTIMIZATION BY LLMS Making use of natural language descriptions. The main advantage of LLMs for optimization is their ability of understanding natural language, which allows people to describe their optimization tasks without formal specifications. For instance, in prompt optimization where the goal is to find a prompt that optimizes the task accuracy, the task can be described with a high-level text summary along with input-output examples. Trading off exploration and exploitation. The exploration-exploitation trade-off is a fundamental challenge in optimization, and it is important for LLMs serving as optimizers to balance these two competing goals. This means that the LLM should be able to exploit promising areas of the search space where good solutions are already found, while also exploring new regions of the search space so as to not miss potentially better solutions. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Authors: Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.2 Main Results 5.3 Ablation Studies 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 6 Related Work 7 Conclusion, Acknowledgments and References 7 Conclusion, Acknowledgments and References A Some Failure Cases A Some Failure Cases B Prompting Formats for Scorer Llm B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions 2 OPRO: LLM AS THE OPTIMIZER Figure 2 illustrates the overall framework of OPRO. In each optimization step, the LLM generates candidate solutions to the optimization task based on the optimization problem description and previously evaluated solutions in the meta-prompt. Then the new solutions are evaluated and added to the meta-prompt for the subsequent optimization process. The optimization process terminates when the LLM is unable to propose new solutions with better optimization scores, or a maximum number of optimization steps has reached. We first outline the desired features of LLMs for optimization, then describe the key design choices based on these desirables. 2.1 DESIRABLES OF OPTIMIZATION BY LLMS Making use of natural language descriptions. The main advantage of LLMs for optimization is their ability of understanding natural language, which allows people to describe their optimization tasks without formal specifications. For instance, in prompt optimization where the goal is to find a prompt that optimizes the task accuracy, the task can be described with a high-level text summary along with input-output examples. Making use of natural language descriptions. Trading off exploration and exploitation. The exploration-exploitation trade-off is a fundamental challenge in optimization, and it is important for LLMs serving as optimizers to balance these two competing goals. This means that the LLM should be able to exploit promising areas of the search space where good solutions are already found, while also exploring new regions of the search space so as to not miss potentially better solutions. Trading off exploration and exploitation. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

The OPRO Framework: Using Large Language Models as Optimizers

Authors:
(1) Chengrun Yang, Google DeepMind and Equal contribution;
(2) Xuezhi Wang, Google DeepMind;
(3) Yifeng Lu, Google DeepMind;
(4) Hanxiao Liu, Google DeepMind;
(5) Quoc V. Le, Google DeepMind;
(6) Denny Zhou, Google DeepMind;
(7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 7 Conclusion, Acknowledgments and References A Some Failure Cases B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions ABSTRACT Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks. Code at https://github.com/ google-deepmind/opro. Table 1: Top instructions with the highest GSM8K zero-shot test accuracies from prompt optimization with different optimizer LLMs. All results use the pre-trained PaLM 2-L as the scorer 1 INTRODUCTION Optimization is critical for all areas. Many optimization techniques are iterative: the optimization starts from an initial solution, then iteratively updates the solution to optimize the objective function (Amari, 1993; Qian, 1999; Kingma & Ba, 2015; Bäck & Schwefel, 1993; Rios & Sahinidis, 2013; Reeves, 1993). The optimization algorithm typically needs to be customized for an individual task to deal with the specific challenges posed by the decision space and the performance landscape, especially for derivative-free optimization. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to utilize large language models (LLMs) as optimizers. With the advancement of prompting techniques, LLMs have achieved impressive performance on a variety of domains (Wei et al., 2022; Kojima et al., 2022; Wang et al., 2022; Zhou et al., 2022a; Madaan et al., 2023; Bai et al., 2022; Chen et al., 2023e). Their ability to understand natural language lays out a new possibility for optimization: instead of formally defining the optimization problem and deriving the update step with a programmed solver, we describe the optimization problem in natural language, then instruct the LLM to iteratively generate new solutions based on the problem description and the previously found solutions. Optimization with LLMs enables quick adaptation to different tasks by changing the problem description in the prompt, and the optimization process can be customized by adding instructions to specify the desired properties of the solutions. To demonstrate the potential of LLMs for optimization, we first present case studies on linear regression and the traveling salesman problem, which are two classic optimization problems that underpin many others in mathematical optimization, computer science, and operations research. On small-scale optimization problems, we show that LLMs are able to find good-quality solutions simply through prompting, and sometimes match or surpass hand-designed heuristic algorithms. Next, we demonstrate the ability of LLMs to optimize prompts: the optimization goal is to find a prompt that maximizes the task accuracy. Specifically, we focus on natural language processing tasks where both the task input and output are in text formats. LLMs are shown to be sensitive to the prompt format (Zhao et al., 2021; Lu et al., 2021; Wei et al., 2023; Madaan & Yazdanbakhsh, 2022); in particular, semantically similar prompts may have drastically different performance (Kojima et al., 2022; Zhou et al., 2022b; Zhang et al., 2023), and the optimal prompt formats can be model-specific and task-specific (Ma et al., 2023; Chen et al., 2023c). Therefore, prompt engineering is often important for LLMs to achieve good performance (Reynolds & McDonell, 2021). However, the large and discrete prompt space makes it challenging for optimization, especially when only API access to the LLM is available. Following prior work on continuous and discrete prompt optimization (Lester et al., 2021; Li & Liang, 2021; Zhou et al., 2022b; Pryzant et al., 2023), we assume a training set is available to compute the training accuracy as the objective value for optimization, and we show in experiments that optimizing the prompt for accuracy on a small training set is sufficient to reach high performance on the test set. The prompt to the LLM serves as a call to the optimizer, and we name it the meta-prompt. Figure 3 shows an example. The meta-prompt contains two core pieces of information. The first piece is previously generated prompts with their corresponding training accuracies. The second piece is the optimization problem description, which includes several exemplars randomly selected from the training set to exemplify the task of interest. We also provide instructions for the LLM to understand the relationships among different parts and the desired output format. Different from recent work on using LLMs for automatic prompt generation (Zhou et al., 2022b; Pryzant et al., 2023), each optimization step in our work generates new prompts that aim to increase the test accuracy based on a trajectory of previously generated prompts, instead of editing one input prompt according to natural language feedback (Pryzant et al., 2023) or requiring the new prompt to follow the same semantic meaning (Zhou et al., 2022b). Making use of the full optimization trajectory, OPRO enables the LLM to gradually generate new prompts that improve the task accuracy throughout the optimization process, where the initial prompts have low task accuracies. We conduct comprehensive evaluation on several LLMs, including text-bison [1] and Palm 2-L in the PaLM-2 model family (Anil et al., 2023), as well as gpt-3.5-turbo and gpt-4 in the GPT model family [2]. We optimize prompts on GSM8K (Cobbe et al., 2021) and Big-Bench Hard (Suzgun et al., 2022), which are reasoning benchmarks where prompting techniques have achieved remarkable performance breakthrough (Wei et al., 2022; Kojima et al., 2022; Suzgun et al., 2022). Starting from initial prompts with low task accuracies, we show that all LLMs in our evaluation are able to serve as optimizers, which consistently improve the performance of the generated prompts through iterative optimization until convergence (see Figure 1). In particular, while these LLMs generally produce instructions of different styles (see Table 1), with zero-shot prompting, their best generated instructions match the few-shot chain-of-thought prompting performance when applied to PaLM 2-L (Anil et al., 2023), outperforming the zero-shot performance with human-designed prompts by up to 8% on GSM8K. Additionally, we observe that the OPRO-optimized prompts transfer to other benchmarks of the same domain and also deliver notable performance gain. This paper is available on arxiv under CC0 1.0 DEED license. [1] Available here: https://cloud.google.com/vertex-ai/docs/generative-ai/learn/ models. [2] Available here: http://openai.com/api/. This work uses gpt-3.5-turbo-0613 and gpt-4-0613. Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Authors: Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.2 Main Results 5.3 Ablation Studies 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 6 Related Work 7 Conclusion, Acknowledgments and References 7 Conclusion, Acknowledgments and References A Some Failure Cases A Some Failure Cases B Prompting Formats for Scorer Llm B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions ABSTRACT Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks. Code at https://github.com/ google-deepmind/opro. Table 1: Top instructions with the highest GSM8K zero-shot test accuracies from prompt optimization with different optimizer LLMs. All results use the pre-trained PaLM 2-L as the scorer 1 INTRODUCTION Optimization is critical for all areas. Many optimization techniques are iterative: the optimization starts from an initial solution, then iteratively updates the solution to optimize the objective function (Amari, 1993; Qian, 1999; Kingma & Ba, 2015; Bäck & Schwefel, 1993; Rios & Sahinidis, 2013; Reeves, 1993). The optimization algorithm typically needs to be customized for an individual task to deal with the specific challenges posed by the decision space and the performance landscape, especially for derivative-free optimization. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to utilize large language models (LLMs) as optimizers. With the advancement of prompting techniques, LLMs have achieved impressive performance on a variety of domains (Wei et al., 2022; Kojima et al., 2022; Wang et al., 2022; Zhou et al., 2022a; Madaan et al., 2023; Bai et al., 2022; Chen et al., 2023e). Their ability to understand natural language lays out a new possibility for optimization: instead of formally defining the optimization problem and deriving the update step with a programmed solver, we describe the optimization problem in natural language, then instruct the LLM to iteratively generate new solutions based on the problem description and the previously found solutions. Optimization with LLMs enables quick adaptation to different tasks by changing the problem description in the prompt, and the optimization process can be customized by adding instructions to specify the desired properties of the solutions. To demonstrate the potential of LLMs for optimization, we first present case studies on linear regression and the traveling salesman problem, which are two classic optimization problems that underpin many others in mathematical optimization, computer science, and operations research. On small-scale optimization problems, we show that LLMs are able to find good-quality solutions simply through prompting, and sometimes match or surpass hand-designed heuristic algorithms. Next, we demonstrate the ability of LLMs to optimize prompts: the optimization goal is to find a prompt that maximizes the task accuracy. Specifically, we focus on natural language processing tasks where both the task input and output are in text formats. LLMs are shown to be sensitive to the prompt format (Zhao et al., 2021; Lu et al., 2021; Wei et al., 2023; Madaan & Yazdanbakhsh, 2022); in particular, semantically similar prompts may have drastically different performance (Kojima et al., 2022; Zhou et al., 2022b; Zhang et al., 2023), and the optimal prompt formats can be model-specific and task-specific (Ma et al., 2023; Chen et al., 2023c). Therefore, prompt engineering is often important for LLMs to achieve good performance (Reynolds & McDonell, 2021). However, the large and discrete prompt space makes it challenging for optimization, especially when only API access to the LLM is available. Following prior work on continuous and discrete prompt optimization (Lester et al., 2021; Li & Liang, 2021; Zhou et al., 2022b; Pryzant et al., 2023), we assume a training set is available to compute the training accuracy as the objective value for optimization, and we show in experiments that optimizing the prompt for accuracy on a small training set is sufficient to reach high performance on the test set. The prompt to the LLM serves as a call to the optimizer, and we name it the meta-prompt. Figure 3 shows an example. The meta-prompt contains two core pieces of information. The first piece is previously generated prompts with their corresponding training accuracies. The second piece is the optimization problem description, which includes several exemplars randomly selected from the training set to exemplify the task of interest. We also provide instructions for the LLM to understand the relationships among different parts and the desired output format. Different from recent work on using LLMs for automatic prompt generation (Zhou et al., 2022b; Pryzant et al., 2023), each optimization step in our work generates new prompts that aim to increase the test accuracy based on a trajectory of previously generated prompts, instead of editing one input prompt according to natural language feedback (Pryzant et al., 2023) or requiring the new prompt to follow the same semantic meaning (Zhou et al., 2022b). Making use of the full optimization trajectory, OPRO enables the LLM to gradually generate new prompts that improve the task accuracy throughout the optimization process, where the initial prompts have low task accuracies. We conduct comprehensive evaluation on several LLMs, including text-bison [1] and Palm 2-L in the PaLM-2 model family (Anil et al., 2023), as well as gpt-3.5-turbo and gpt-4 in the GPT model family [2]. We optimize prompts on GSM8K (Cobbe et al., 2021) and Big-Bench Hard (Suzgun et al., 2022), which are reasoning benchmarks where prompting techniques have achieved remarkable performance breakthrough (Wei et al., 2022; Kojima et al., 2022; Suzgun et al., 2022). Starting from initial prompts with low task accuracies, we show that all LLMs in our evaluation are able to serve as optimizers, which consistently improve the performance of the generated prompts through iterative optimization until convergence (see Figure 1). In particular, while these LLMs generally produce instructions of different styles (see Table 1), with zero-shot prompting, their best generated instructions match the few-shot chain-of-thought prompting performance when applied to PaLM 2-L (Anil et al., 2023), outperforming the zero-shot performance with human-designed prompts by up to 8% on GSM8K. Additionally, we observe that the OPRO-optimized prompts transfer to other benchmarks of the same domain and also deliver notable performance gain. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv [1] Available here: https://cloud.google.com/vertex-ai/docs/generative-ai/learn/ models. [2] Available here: http://openai.com/api/. This work uses gpt-3.5-turbo-0613 and gpt-4-0613.

Large Language Models as Optimizers

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