“The False Promise of Imitating Proprietary LLMs”

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right-0 bottom-0 left-0 "> <img class="cover-img__image relative w-full h-full object-cover" src="https://media.licdn.com/dms/image/v2/D4E12AQG673-CYo6GaQ/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1688647012856?e=2147483647&v=beta&t=fdLEGglyN2y0rpZWLhZL9sisGN5rqWU2LpC6t_QCDKM" fetchpriority="high" data-embed-id="cover-image" alt="“The False Promise of Imitating Proprietary LLMs” — A Provectus Perspective"> </div> </div> <figcaption class="cover-img__caption text-sm text-color-text-secondary pt-2">Provectus AI review #2</figcaption> </figure> <header> <section class="core-section-container relative my-3"> <h1 class="pulse-title text-display-md text-color-text lg:text-display-lg font-bold w-11/12">“The False Promise of Imitating Proprietary LLMs” — A Provectus Perspective</h1> <div class="ellipsis-menu absolute right-0 top-0"> <div class="collapsible-dropdown flex items-center relative hyphens-auto"> <button class="ellipsis-menu__trigger 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</div> </div> <div class="core-section-container__content break-words"> <div class="base-card relative w-full hover:no-underline focus:no-underline base-card--link base-main-card flex flex-wrap py-2 pr-2 babybear:pr-0 base-main-card--link publisher-author-card"> <a class="base-card__full-link absolute top-0 right-0 bottom-0 left-0 p-0 z-[2]" href="https://www.linkedin.com/company/provectus-it-inc" data-tracking-control-name="article-ssr-frontend-pulse_publisher-author-card" data-tracking-will-navigate> <span class="sr-only"> Provectus </span> </a> <img class="inline-block relative w-6 h-6 " data-delayed-url="https://media.licdn.com/dms/image/v2/D4D0BAQFnf7MoIbtEqQ/company-logo_100_100/company-logo_100_100/0/1693390195179/provectus_it_inc_logo?e=2147483647&v=beta&t=s9McVt3w2eaUsBGzMCIDrDlFLSzhRBG8X4vcNwtzC0Y" data-ghost-classes="bg-color-entity-ghost-background" data-ghost-url="https://static.licdn.com/aero-v1/sc/h/cs8pjfgyw96g44ln9r7tct85f" alt="Provectus"> <div class="base-main-card__info self-center ml-1 flex-1 relative break-words papabear:min-w-0 mamabear:min-w-0 babybear:w-full ">  <h3 class="base-main-card__title font-sans text-[18px] font-bold text-color-text overflow-hidden "> Provectus </h3> <h4 class="base-main-card__subtitle body-text text-color-text overflow-hidden "> We help businesses leverage cloud, data, and AI to reimagine the way they operate, compete, and deliver customer value. </h4>  <div class="body-text text-color-text-low-emphasis base-main-card__metadata"> Published Jul 6, 2023 </div>  </div> <div class="base-main-card__ctas z-[3] self-center ml-3 babybear:ml-1 babybear:self-start"> <a class="base-main-card__cta btn-sm btn-secondary" href="https://www.linkedin.com/signup/cold-join?session_redirect=%2Fpulse%2Ffalse-promise-imitating-proprietary-llms-provectus-perspective%3Ftrk%3Darticle-ssr-frontend-pulse_little-text-block&trk=article-ssr-frontend-pulse_publisher-author-card" data-tracking-control-name="article-ssr-frontend-pulse_publisher-author-card" data-tracking-will-navigate> + Follow </a> </div> </div> </div> </section> </header> <div data-test-id="article-content-blocks">  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The AI/ML market is incredibly dynamic, making it challenging to keep up. At Provectus, we strive to stay abreast of the latest news and updates, delve into recent papers, and share the essence of our findings with the community.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In this installment of the "Provectus AI Review" series, we share our perspective on a paper titled </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2305%2E15717&urlhash=1odr&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>The False Promise of Imitating Proprietary LLMs </a></span><span class>, authored by </span><span class> <a href="https://www.linkedin.com/in/arnavgudibande?trk=article-ssr-frontend-pulse_little-mention" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-mention" data-tracking-will-navigate data-test-link> Arnav Gudibande </a>  </span><span class> et al. at </span><span class>  <a href="https://www.linkedin.com/school/uc-berkeley/?trk=article-ssr-frontend-pulse_little-mention" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-mention" data-tracking-will-navigate data-test-link>University of California, Berkeley </a>  </span><span class>.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Abstract</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>To start, let's examine the abstract of the paper.</span> </p> </div>  <div class="flex flex-col mt-2" data-test-id="publishing-image-block" rel="ugc"> <figure> <img alt="No alt text provided for this image" class="lazy-load block w-full" data-delayed-url="https://media.licdn.com/dms/image/v2/D4E12AQF176t4EYYZLQ/article-inline_image-shrink_1000_1488/article-inline_image-shrink_1000_1488/0/1688647148694?e=2147483647&v=beta&t=BuTVcCAwD6qMTBA6LTg4FcvnrT_wddL_nUwkFajJ-78"> <figcaption class="mt-[0.8rem] text-center text-sm font-normal text-color-text-low-emphasis" data-test-id="publishing-image-block-caption"> <span class>https://arxiv.org/pdf/2305.15717.pdf</span> </figcaption> </figure> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In the paper, the authors evaluate the approach of finetuning weaker language models on outputs from stronger proprietary models like ChatGPT, to imitate their capabilities in a cost-efficient manner. They conducted an experiment where they finetuned Language Models (LMs) to imitate ChatGPT using varying base model sizes, data sources, and imitation data amounts. These models were evaluated using crowd raters and canonical NLP benchmarks.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The initial results were promising, with the imitation models appearing to follow instructions well and their outputs rated as competitive with ChatGPT by crowd workers. However, more targeted automatic evaluations revealed that the imitation models did not significantly close the gap between the base LM and ChatGPT on tasks that were not heavily supported in the imitation data. The authors found that the imitation models were good at mimicking ChatGPT's style, but not its factuality.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The authors concluded that </span><span class="font-[700]">model imitation is a false promise</span><span class>. There is a substantial capability gap between open and closed LMs that cannot be bridged using current methods without an unwieldy amount of imitation data or more capable base LMs. Therefore, they argue that the most effective way to improve open-source models is to focus on developing better base LMs, rather than attempting to imitate proprietary systems.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Major Takeaways and Provectus Perspective</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In this section, we will delve into some of the points raised in the paper and share our thoughts on potential solutions.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>#1</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <blockquote class="w-auto"><span class="italic">We consider decoder-only models ranging in size from 1.5B to 13B parameters: GPT-2 1.5B (Radford et al., 2019), LLaMA 7B (Touvron et al., 2023), and LLaMA 13B.</span></blockquote> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In practical terms, this suggests that alternative architectures, such as encoder-decoder models, should also be explored and evaluated. For instance, we could consider models like Google's UL2 or T5. It would also be beneficial to provide some budget estimates for these potential explorations.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>#2</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <blockquote class="w-auto"><span class="italic">For human evaluation, we conduct blind pairwise output comparisons using Mechanical Turk. In our UI, we present each rater with a task instruction and the output of two unknown models, one of which is ChatGPT and the other is one of our imitation models.</span></blockquote> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Every significant project requires a tool for human assessment that allows for a seamless switch between a private workforce and a public crowd. A lot of open-source solutions that do exactly this already exist on the market. For example, </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fgithub%2Ecom%2Fray-project%2Faviary&urlhash=CfKo&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Ray's Aviary </a></span><span class>, which was recently released, could prove to be a useful tool in this context.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>#3</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <blockquote class="w-auto"><span class="italic">Training local imitation models is far more successful… This demonstrates that it is far more feasible to distill a specific behavior from ChatGPT as opposed to broadly matching its capabilities.</span></blockquote> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In practical terms, this implies that the approach can be utilized for a broad spectrum of specific tasks that extend beyond mere replication of ChatGPT.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">For example:</span><span class> Fine-tuning OpenAI models for English-to-Cypher translation can be quite costly. We can argue that a well-tuned open-source LLM, utilizing the imitation approach, could be more feasible for continuous improvement in the translation task, without incurring the high costs of fine-tuning.</span> </p> </div> <div class="inline-articles bg-color-surface-new border-hidden rounded-sm p-1 mx-auto my-1.5 mamabear:w-2/3 inline-articles__after"> <h2 class="text-xl font-medium ml-1.5 my-1 text-color-text mamabear:text-center">Recommended by LinkedIn</h2> <div class="flex flex-wrap mx-0.5 babybear:mx-1.5"> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/how-bypass-gptzero-12-proven-techniques-beat-ai-detection-gautam-fokic"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D5612AQFuDzQCH0AI9Q/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1719719253733?e=2147483647&v=beta&t=s0bzOES7IqRPAocWcsyHEa9v8TC6NPm_fpaSQnPoQgo"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">How to Bypass GPTZero: 12 Proven Techniques to Beat AI…</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> Parul Gautam </span> <span class="before:middot pt-0.5 inline-article-published-date"> 4 months ago </span> </div> </a>  </div> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/can-gptzero-relied-upon-ai-detection-accuracy-anna-yuliani-hd6kf"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D4D12AQEU7OoN6GIpyA/article-cover_image-shrink_720_1280/article-cover_image-shrink_720_1280/0/1715758304981?e=2147483647&v=beta&t=EqVlArIevFGzvrRO8bX36c0naOxvsV57Xz4liW-IPkg"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">Can GPTZero be relied upon for AI Detection accuracy?</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> Anna Y. </span> <span class="before:middot pt-0.5 inline-article-published-date"> 6 months ago </span> </div> </a>  </div> <div class="content-author-card flex flex-col max-w-[270px] h-auto rounded-md border-1 border-system-gray-30 border-solid pb-1.5 inline-article mx-auto my-1 border-hidden papabear:w-[219px] babybear:min-w-full mamabear:min-w-full h-auto bg-color-canvas-tint"> <a class="no-underline" data-tracking-control-name="inline-recommended-articles" data-tracking-will-navigate href="https://www.linkedin.com/pulse/automl-gpt-causal-reasoning-llms-metagpt-free-access-gpt-4-butvinik"> <img class="content-image w-full h-[150px] bg-cover rounded-t-md object-cover" data-delayed-url="https://media.licdn.com/dms/image/v2/D4D12AQG0cW5Htyk2kg/article-cover_image-shrink_600_2000/article-cover_image-shrink_600_2000/0/1683451626006?e=2147483647&v=beta&t=Ph6iaC2xnOwnRtYVKflsSLXuwDYIvzd2z1xSnJUYs8A"> <div class="inline-article-title text-md text-color-text font-bold pl-2 pt-1 pr-1.5">AutoML-GPT; Causal Reasoning and LLMs; MetaGPT; Free…</div> <div class="inline-article-meta px-2 mt-1 font-sans text-xs font-normal text-color-text-low-emphasis h-full"> <span class="inline-article-author pt-0.5"> Danny Butvinik </span> <span class="before:middot pt-0.5 inline-article-published-date"> 1 year ago </span> </div> </a>  </div> </div> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>#4</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <blockquote class="w-auto"><span class="italic">An open problem is whether these performance regressions can be mitigated using regularization or by mixing in pre-training data during fine-tuning.</span></blockquote> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Here is an interesting observation: the authors highlight an issue that arises when you fine-tune a model on conversational-style data, but then apply a different benchmark for evaluation.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>#5</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <blockquote class="w-auto"><span class="italic">However, it also may be possible to use the target model to perform RLHF or constitutional AI… to further improve results. Lastly, we only considered relatively simple methods for collecting imitation data, however, there may be more advanced methods (e.g., active learning) that may improve the effectiveness or efficiency of model imitation.</span></blockquote> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Here is another noteworthy point: the authors highlight that despite certain constraints, there are still lots of methods to explore.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Note:</span><span class> according to OpenAI's terms of use, any dataset sampled from their models technically renders the fine-tuned LLM unusable for commercial purposes.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <h2 data-test-id="pulse-publishing-h1"> <span class>Conclusion</span> </h2> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>In </span><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2305%2E15717&urlhash=1odr&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>The False Promise of Imitating Proprietary LLMs </a></span><span class>, the authors evaluate the effectiveness of model imitation as means of enhancing open-source Language Models (LMs). The findings suggest that businesses can gain a competitive edge by pre-training powerful base models, but also that one group can mimic another's model if their base LMs are equally competent. The study raises questions about the future of human evaluation, about improving open-source LMs, and ethical and legal issues surrounding the use of proprietary models by the open-source community.</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>The </span><span class>  <a href="https://www.linkedin.com/company/provectus-it-inc?trk=article-ssr-frontend-pulse_little-mention" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-mention" data-tracking-will-navigate data-test-link>Provectus </a>  </span><span class> perspective on the paper is clear: it is definitely worth a read. Publications like this are always extremely valuable as they go through a thorough evaluation process. There are still many opportunities - and gaps, however - that can and should be investigated in and around the topic.</span> </p> </div>  <hr data-test-id="publishing-divider-block" class="my-[3.2rem] mx-auto h-[1px] border-0 bg-black-a16">  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class="font-[700]">Authors:</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <a href="https://www.linkedin.com/in/gareev?trk=article-ssr-frontend-pulse_little-mention" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-mention" data-tracking-will-navigate data-test-link> Rinat Gareev </a>  </span><span class> — Senior Solutions Architect || Provectus</span> </p> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class><a href="https://www.linkedin.com/in/marlon-cajamarca-vega-235577b7?miniProfileUrn=urn%3Ali%3Afs_miniProfile%3AACoAABjSk4kBO9g03CRalm4TflbLkeQQmf_swTA&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Marlon Cajamarca Vega </a></span><span class> — ML Engineer & AI Educator || Provectus</span> </p> </div>  <hr data-test-id="publishing-divider-block" class="my-[3.2rem] mx-auto h-[1px] border-0 bg-black-a16">  <div class="article-main__content" data-test-id="publishing-text-block"> <h3><span class>Moving Forward — Learn more about Provectus AI expertise</span></h3> </div>  <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class> <ol> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fblog%2Fcomparison-large-language-models-biomedical-domain%2F&urlhash=oZyj&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>A Comparison of Large Language Models (LLMs) in Biomedical Domain </a></span></li> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fblog%2Fan-instruction-following-gpt-j-model-based-on-instructions-generated-by-bloom%2F&urlhash=3xIa&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>An Instruction-following GPT-J Model Based on Instructions Generated by Bloom </a></span></li> <li><span class><a href="https://www.linkedin.com/redir/redirect?url=https%3A%2F%2Fprovectus%2Ecom%2Fblog%2Fexploring-intelligent-search-solutions-a-comparative-analysis-of-amazon-kendra-integration-and-large-language-model-crawlers%2F&urlhash=12I4&trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Exploring Intelligent Search Solutions: A Comparative Analysis of Amazon Kendra Integration and Large Language ModelCrawlers </a></span></li> <li><span class><a href="https://www.linkedin.com/pulse/google-io-2023-journey-future-ai-technology-provectus-it-inc/?trk=article-ssr-frontend-pulse_little-text-block" target="_blank" data-tracking-control-name="article-ssr-frontend-pulse_little-text-block" data-tracking-will-navigate data-test-link>Provectus AI review 1 — Google I/O 2023 Overview </a></span></li> </ol> </span> </p> </div>  <div class="article-main__content" 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