{"meta":{"page":{"currentPage":1,"from":1,"lastPage":7,"perPage":4,"to":4,"total":28}},"jsonapi":{"version":"1.0"},"links":{"first":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications?filter%5Bpublished%5D=true&filter%5Btopics%5D%5Bslug%5D%5B0%5D=language&filter%5Bexclude%5D%5B0%5D=189&include=media&page%5Bnumber%5D=1&page%5Bsize%5D=4&sort=-publicationDate%2C-createdAt","last":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications?filter%5Bpublished%5D=true&filter%5Btopics%5D%5Bslug%5D%5B0%5D=language&filter%5Bexclude%5D%5B0%5D=189&include=media&page%5Bnumber%5D=7&page%5Bsize%5D=4&sort=-publicationDate%2C-createdAt","next":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications?filter%5Bpublished%5D=true&filter%5Btopics%5D%5Bslug%5D%5B0%5D=language&filter%5Bexclude%5D%5B0%5D=189&include=media&page%5Bnumber%5D=2&page%5Bsize%5D=4&sort=-publicationDate%2C-createdAt"},"data":[{"type":"research-publications","id":"162","attributes":{"published":true,"createdAt":"2023-01-17T21:50:41.000000Z","updatedAt":"2023-02-10T13:18:29.000000Z","publicationDateFormatted":"January 11, 2023","publicationDate":"2023-01-11T00:00:00-08:00","slug":"forecasting-misuse","title":"Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk","description":"OpenAI researchers collaborated with Georgetown University\u2019s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research. This report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and introduces a framework for analyzing potential mitigations. Read the full report\u00a0here.","descriptionRichText":{"type":"div","props":[],"children":[{"type":"p","props":[],"children":["OpenAI researchers collaborated with Georgetown University\u2019s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research. This report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and introduces a framework for analyzing potential mitigations. Read the full report\u00a0",{"type":"a","props":{"href":"https:\/\/arxiv.org\/abs\/2301.04246","rel":"noopener noreferrer","target":"_blank"},"children":["here"]},".",{"type":"br","props":{"class":"softbreak"},"children":[]}]}]},"heroBlendMode":"default","template":"level-1","colorTheme":"mid-brown","releaseSummary":null,"releaseWhyItMatters":null,"releaseSafety":null,"chapters":false,"publicationAbstract":null,"publicationAbstractRichText":null,"seo":{"ogImageSrc":"https:\/\/openaicom.imgix.net\/2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2304%2C2304","ogImageAlt":"Forecasting Misuse","twitterImageSrc":null,"twitterImageAlt":null,"noindex":false,"description":"OpenAI researchers collaborated with Georgetown University\u2019s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research. This report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and introduces a framework for analyzing potential mitigations. Read the full report\u00a0here.","ogTitle":"Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk","ogDescription":"OpenAI researchers collaborated with Georgetown University\u2019s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research. This report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and introduces a framework for analyzing potential mitigations. Read the full report\u00a0here.","twitterTitle":null,"twitterDescription":null,"twitterCardType":"summary_large_image"},"acknowledgments":{"title1":"Report authors","text1":"

Josh A. Goldstein (Georgetown University\u2019s Center for Security and Emerging Technology)
Girish Sastry (OpenAI)
Micah Musser (Center for Security and Emerging Technology)
Ren\u00e9e DiResta (Stanford Internet Observatory)
Matthew Gentzel (Longview Philanthropy) (work done at OpenAI)
Katerina Sedova (US Department of State) (work done at Center for Security and Emerging Technology prior to government service)
<\/p>","title2":null,"text2":null}},"relationships":{"blocks":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/blocks","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/blocks"}},"media":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/media","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/media"},"data":[{"type":"media","id":"13180","meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png"}},{"type":"media","id":"13181","meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png"}},{"type":"media","id":"13182","meta":{"role":"cover","crop":"listing","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png"}}],"meta":{"roles":{"cover":{"+1 (No bleed)":["13180"],"+2 (Full bleed)":["13181"],"listing":["13182"]}}}},"files":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/files","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/files"}},"related-items":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/related-items","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/related-items"}},"ctas":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/ctas","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/ctas"}},"research-publication-links":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/research-publication-links","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/research-publication-links"}},"author-groups":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/author-groups","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/author-groups"}},"topics":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/topics","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/topics"}},"models":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/models","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/models"}},"content-types":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/content-types","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/content-types"}},"footnotes":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/footnotes","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/footnotes"}},"references":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/references","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162\/relationships\/references"}}},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/162"}},{"type":"research-publications","id":"111","attributes":{"published":true,"createdAt":"2023-01-10T01:30:59.000000Z","updatedAt":"2023-01-20T22:20:57.000000Z","publicationDateFormatted":"July 28, 2022","publicationDate":"2022-07-28T00:00:00-07:00","slug":"efficient-training-of-language-models-to-fill-in-the-middle","title":"Efficient training of language models to fill in the middle","description":"","descriptionRichText":null,"heroBlendMode":"default","template":"level-0","colorTheme":null,"releaseSummary":null,"releaseWhyItMatters":null,"releaseSafety":null,"chapters":false,"publicationAbstract":"

We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end. While this data augmentation has garnered much interest in recent years, we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales. Given the usefulness, simplicity, and efficiency of training models to fill-in-the-middle (FIM), we suggest that future autoregressive language models be trained with FIM by default. To this end, we run a series of ablations on key hyperparameters, such as the data transformation frequency, the structure of the transformation, and the method of selecting the infill span. We use these ablations to prescribe strong default settings and best practices to train FIM models. We have released our best infilling model trained with best practices in our API, and release our infilling benchmarks to aid future research.
<\/p>","publicationAbstractRichText":{"type":"div","props":[],"children":[{"type":"p","props":[],"children":["We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end. While this data augmentation has garnered much interest in recent years, we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales. Given the usefulness, simplicity, and efficiency of training models to fill-in-the-middle (FIM), we suggest that future autoregressive language models be trained with FIM by default. To this end, we run a series of ablations on key hyperparameters, such as the data transformation frequency, the structure of the transformation, and the method of selecting the infill span. We use these ablations to prescribe strong default settings and best practices to train FIM models. We have released our best infilling model trained with best practices in our API, and release our infilling benchmarks to aid future research.",{"type":"br","props":{"class":"softbreak"},"children":[]}]}]},"seo":{"ogImageSrc":"https:\/\/openaicom.imgix.net\/78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","ogImageAlt":"Efficient Training Of Language Models To Fill In The Middle","twitterImageSrc":null,"twitterImageAlt":null,"noindex":false,"description":"","ogTitle":"Efficient training of language models to fill in the middle","ogDescription":"","twitterTitle":null,"twitterDescription":null,"twitterCardType":"summary_large_image"},"acknowledgments":{"title1":null,"text1":null,"title2":null,"text2":null}},"relationships":{"blocks":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/blocks","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/blocks"}},"media":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/media","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/media"},"data":[{"type":"media","id":"7779","meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png"}},{"type":"media","id":"7780","meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png"}},{"type":"media","id":"7781","meta":{"role":"cover","crop":"listing","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png"}}],"meta":{"roles":{"cover":{"+1 (No bleed)":["7779"],"+2 (Full bleed)":["7780"],"listing":["7781"]}}}},"files":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/files","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/files"}},"related-items":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/related-items","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/related-items"}},"ctas":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/ctas","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/ctas"}},"research-publication-links":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/research-publication-links","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/research-publication-links"}},"author-groups":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/author-groups","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/author-groups"}},"topics":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/topics","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/topics"}},"models":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/models","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/models"}},"content-types":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/content-types","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/content-types"}},"footnotes":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/footnotes","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/footnotes"}},"references":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/references","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111\/relationships\/references"}}},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/111"}},{"type":"research-publications","id":"158","attributes":{"published":true,"createdAt":"2023-01-11T01:23:00.000000Z","updatedAt":"2023-01-20T22:21:26.000000Z","publicationDateFormatted":"July 25, 2022","publicationDate":"2022-07-25T00:00:00-07:00","slug":"a-hazard-analysis-framework-for-code-synthesis-large-language-models","title":"A hazard analysis framework for code synthesis large language models","description":"","descriptionRichText":null,"heroBlendMode":"default","template":"level-0","colorTheme":null,"releaseSummary":null,"releaseWhyItMatters":null,"releaseSafety":null,"chapters":false,"publicationAbstract":"

Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase the rate of progress in technical fields that may themselves have destabilizing impacts or have misuse potential. Yet such safety impacts are not yet known or remain to be explored. In this paper, we outline a hazard analysis framework constructed at OpenAI to uncover hazards or safety risks that the deployment of models like Codex may impose technically, socially, politically, and economically. The analysis is informed by a novel evaluation framework that determines the capacity of advanced code generation techniques against the complexity and expressivity of specification prompts, and their capability to understand and execute them relative to human ability.
<\/p>","publicationAbstractRichText":{"type":"div","props":[],"children":[{"type":"p","props":[],"children":["Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code. Although Codex provides a plethora of benefits, models that may generate code on such scale have significant limitations, alignment problems, the potential to be misused, and the possibility to increase the rate of progress in technical fields that may themselves have destabilizing impacts or have misuse potential. Yet such safety impacts are not yet known or remain to be explored. In this paper, we outline a hazard analysis framework constructed at OpenAI to uncover hazards or safety risks that the deployment of models like Codex may impose technically, socially, politically, and economically. The analysis is informed by a novel evaluation framework that determines the capacity of advanced code generation techniques against the complexity and expressivity of specification prompts, and their capability to understand and execute them relative to human ability.",{"type":"br","props":{"class":"softbreak"},"children":[]}]}]},"seo":{"ogImageSrc":"https:\/\/openaicom.imgix.net\/453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","ogImageAlt":"A Hazard Analysis Framework For Code Synthesis Large Language Models","twitterImageSrc":null,"twitterImageAlt":null,"noindex":false,"description":"","ogTitle":"A hazard analysis framework for code synthesis large language models","ogDescription":"","twitterTitle":null,"twitterDescription":null,"twitterCardType":"summary_large_image"},"acknowledgments":{"title1":null,"text1":null,"title2":null,"text2":null}},"relationships":{"blocks":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/blocks","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/blocks"}},"media":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/media","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/media"},"data":[{"type":"media","id":"7635","meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png"}},{"type":"media","id":"7636","meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png"}},{"type":"media","id":"7637","meta":{"role":"cover","crop":"listing","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png"}}],"meta":{"roles":{"cover":{"+1 (No bleed)":["7635"],"+2 (Full bleed)":["7636"],"listing":["7637"]}}}},"files":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/files","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/files"}},"related-items":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/related-items","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/related-items"}},"ctas":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/ctas","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/ctas"}},"research-publication-links":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/research-publication-links","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/research-publication-links"}},"author-groups":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/author-groups","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/author-groups"}},"topics":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/topics","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/topics"}},"models":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/models","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/models"}},"content-types":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/content-types","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/content-types"}},"footnotes":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/footnotes","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/footnotes"}},"references":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/references","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158\/relationships\/references"}}},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/158"}},{"type":"research-publications","id":"159","attributes":{"published":true,"createdAt":"2023-01-11T01:31:00.000000Z","updatedAt":"2023-01-20T22:23:54.000000Z","publicationDateFormatted":"June 17, 2022","publicationDate":"2022-06-17T00:00:00-07:00","slug":"evolution-through-large-models","title":"Evolution through large models","description":"","descriptionRichText":null,"heroBlendMode":"default","template":"level-0","colorTheme":null,"releaseSummary":null,"releaseWhyItMatters":null,"releaseSafety":null,"chapters":false,"publicationAbstract":"

This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such evolution through large models (ELM), in the main experiment ELM combined with MAP-Elites generates hundreds of thousands of functional examples of Python programs that output working ambulating robots in the Sodarace domain, which the original LLM had never seen in pre-training. These examples then help to bootstrap training a new conditional language model that can output the right walker for a particular terrain. The ability to bootstrap new models that can output appropriate artifacts for a given context in a domain where zero training data was previously available carries implications for open-endedness, deep learning, and reinforcement learning. These implications are explored here in depth in the hope of inspiring new directions of research now opened up by ELM.
<\/p>","publicationAbstractRichText":{"type":"div","props":[],"children":[{"type":"p","props":[],"children":["This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such evolution through large models (ELM), in the main experiment ELM combined with MAP-Elites generates hundreds of thousands of functional examples of Python programs that output working ambulating robots in the Sodarace domain, which the original LLM had never seen in pre-training. These examples then help to bootstrap training a new conditional language model that can output the right walker for a particular terrain. The ability to bootstrap new models that can output appropriate artifacts for a given context in a domain where zero training data was previously available carries implications for open-endedness, deep learning, and reinforcement learning. These implications are explored here in depth in the hope of inspiring new directions of research now opened up by ELM.",{"type":"br","props":{"class":"softbreak"},"children":[]}]}]},"seo":{"ogImageSrc":"https:\/\/openaicom.imgix.net\/e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","ogImageAlt":"Evolution Through Large Models","twitterImageSrc":null,"twitterImageAlt":null,"noindex":false,"description":"","ogTitle":"Evolution through large models","ogDescription":"","twitterTitle":null,"twitterDescription":null,"twitterCardType":"summary_large_image"},"acknowledgments":{"title1":null,"text1":null,"title2":null,"text2":null}},"relationships":{"blocks":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/blocks","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/blocks"}},"media":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/media","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/media"},"data":[{"type":"media","id":"15597","meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png"}},{"type":"media","id":"15598","meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png"}},{"type":"media","id":"15599","meta":{"role":"cover","crop":"listing","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png"}}],"meta":{"roles":{"cover":{"+1 (No bleed)":["15597"],"+2 (Full bleed)":["15598"],"listing":["15599"]}}}},"files":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/files","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/files"}},"related-items":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/related-items","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/related-items"}},"ctas":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/ctas","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/ctas"}},"research-publication-links":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/research-publication-links","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/research-publication-links"}},"author-groups":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/author-groups","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/author-groups"}},"topics":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/topics","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/topics"}},"models":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/models","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/models"}},"content-types":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/content-types","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/content-types"}},"footnotes":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/footnotes","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/footnotes"}},"references":{"links":{"related":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/references","self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159\/relationships\/references"}}},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/research-publications\/159"}}],"included":[{"type":"media","id":"13180","attributes":{"createdAt":"2023-02-10T13:18:30.000000Z","updatedAt":"2023-02-10T13:18:30.000000Z","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png","filename":"forecasting-misuse.png","role":"cover","crop":"+1 (No bleed)","ratio":"1:1","lqip":null,"src":"https:\/\/openaicom.imgix.net\/2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2304%2C2304","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png","width":2304,"height":2304,"alt":"Forecasting Misuse","caption":"

Justin Jay Wang
<\/p>","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/13180"},"meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png"}},{"type":"media","id":"13181","attributes":{"createdAt":"2023-02-10T13:18:30.000000Z","updatedAt":"2023-02-10T13:18:30.000000Z","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png","filename":"forecasting-misuse.png","role":"cover","crop":"+2 (Full bleed)","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2304%2C2304","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png","width":2304,"height":2304,"alt":"Forecasting Misuse","caption":"

Justin Jay Wang
<\/p>","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/13181"},"meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png"}},{"type":"media","id":"13182","attributes":{"createdAt":"2023-02-10T13:18:30.000000Z","updatedAt":"2023-02-10T13:18:30.000000Z","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png","filename":"forecasting-misuse.png","role":"cover","crop":"listing","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2304%2C2304","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png","width":2304,"height":2304,"alt":"Forecasting Misuse","caption":"

Justin Jay Wang
<\/p>","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/13182"},"meta":{"role":"cover","crop":"listing","uuid":"2e288b87-2d80-4fa6-abea-5d9e650d7143\/forecasting-misuse.png"}},{"type":"media","id":"7779","attributes":{"createdAt":"2023-01-23T23:51:32.000000Z","updatedAt":"2023-01-23T23:51:32.000000Z","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png","filename":"efficient-training-of-language-models-to-fill-in-the-middle.png","role":"cover","crop":"+1 (No bleed)","ratio":"1:1","lqip":null,"src":"https:\/\/openaicom.imgix.net\/78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png","width":2064,"height":2064,"alt":"Efficient Training Of Language Models To Fill In The Middle","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/7779"},"meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png"}},{"type":"media","id":"7780","attributes":{"createdAt":"2023-01-23T23:51:32.000000Z","updatedAt":"2023-01-23T23:51:32.000000Z","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png","filename":"efficient-training-of-language-models-to-fill-in-the-middle.png","role":"cover","crop":"+2 (Full bleed)","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png","width":2064,"height":2064,"alt":"Efficient Training Of Language Models To Fill In The Middle","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/7780"},"meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png"}},{"type":"media","id":"7781","attributes":{"createdAt":"2023-01-23T23:51:32.000000Z","updatedAt":"2023-01-23T23:51:32.000000Z","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png","filename":"efficient-training-of-language-models-to-fill-in-the-middle.png","role":"cover","crop":"listing","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png","width":2064,"height":2064,"alt":"Efficient Training Of Language Models To Fill In The Middle","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/7781"},"meta":{"role":"cover","crop":"listing","uuid":"78474566-f381-41aa-8091-771930f557ab\/efficient-training-of-language-models-to-fill-in-the-middle.png"}},{"type":"media","id":"7635","attributes":{"createdAt":"2023-01-23T23:23:49.000000Z","updatedAt":"2023-01-23T23:23:49.000000Z","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","filename":"a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","role":"cover","crop":"+1 (No bleed)","ratio":"1:1","lqip":null,"src":"https:\/\/openaicom.imgix.net\/453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","width":2064,"height":2064,"alt":"A Hazard Analysis Framework For Code Synthesis Large Language Models","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/7635"},"meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png"}},{"type":"media","id":"7636","attributes":{"createdAt":"2023-01-23T23:23:49.000000Z","updatedAt":"2023-01-23T23:23:49.000000Z","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","filename":"a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","role":"cover","crop":"+2 (Full bleed)","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","width":2064,"height":2064,"alt":"A Hazard Analysis Framework For Code Synthesis Large Language Models","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/7636"},"meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png"}},{"type":"media","id":"7637","attributes":{"createdAt":"2023-01-23T23:23:49.000000Z","updatedAt":"2023-01-23T23:23:49.000000Z","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","filename":"a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","role":"cover","crop":"listing","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png","width":2064,"height":2064,"alt":"A Hazard Analysis Framework For Code Synthesis Large Language Models","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/7637"},"meta":{"role":"cover","crop":"listing","uuid":"453c4f2b-144d-4b4d-aad7-55e43704a23c\/a-hazard-analysis-framework-for-code-synthesis-large-language-models.png"}},{"type":"media","id":"15597","attributes":{"createdAt":"2023-02-14T23:21:08.000000Z","updatedAt":"2023-02-14T23:21:08.000000Z","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png","filename":"evolution-through-large-models.png","role":"cover","crop":"+1 (No bleed)","ratio":"1:1","lqip":null,"src":"https:\/\/openaicom.imgix.net\/e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png","width":2064,"height":2064,"alt":"Evolution Through Large Models","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/15597"},"meta":{"role":"cover","crop":"+1 (No bleed)","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png"}},{"type":"media","id":"15598","attributes":{"createdAt":"2023-02-14T23:21:08.000000Z","updatedAt":"2023-02-14T23:21:08.000000Z","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png","filename":"evolution-through-large-models.png","role":"cover","crop":"+2 (Full bleed)","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png","width":2064,"height":2064,"alt":"Evolution Through Large Models","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/15598"},"meta":{"role":"cover","crop":"+2 (Full bleed)","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png"}},{"type":"media","id":"15599","attributes":{"createdAt":"2023-02-14T23:21:08.000000Z","updatedAt":"2023-02-14T23:21:08.000000Z","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png","filename":"evolution-through-large-models.png","role":"cover","crop":"listing","ratio":"default","lqip":null,"src":"https:\/\/openaicom.imgix.net\/e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png?auto=compress%2Cformat&fit=min&fm=jpg&q=80&rect=0%2C0%2C2064%2C2064","originalSrc":"https:\/\/openaicomproductionae4b.blob.core.windows.net\/production-twill-01\/e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png","width":2064,"height":2064,"alt":"Evolution Through Large Models","caption":"","video":"","metadata":[]},"links":{"self":"https:\/\/openaicom-api-bdcpf8c6d2e9atf6.z01.azurefd.net\/api\/v1\/media\/15599"},"meta":{"role":"cover","crop":"listing","uuid":"e4e06b94-2272-464c-99fa-a10c76484ffb\/evolution-through-large-models.png"}}]}