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has-text-justified",children:[(0,Ue.jsxs)("p",{className:"c18",children:[(0,Ue.jsx)("span",{className:"c9",children:"Introducing "}),(0,Ue.jsx)("strong",{children:"LiveBench"}),(0,Ue.jsx)("span",{className:"c0",children:": a benchmark for LLMs designed with test set contamination and objective evaluation in mind"})]}),(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsx)("span",{className:"c9",children:"LiveBench has the following properties: "})}),(0,Ue.jsxs)("ul",{className:"c28 lst-kix_r92srvgun7j-0 start",children:[(0,Ue.jsx)("li",{className:"c18 c29 li-bullet-0",children:(0,Ue.jsx)("span",{className:"c0",children:"LiveBench is designed to limit potential contamination by releasing new questions monthly, as well as having questions based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses."})}),(0,Ue.jsx)("li",{className:"c18 c29 li-bullet-0",children:(0,Ue.jsx)("span",{className:"c0",children:"Each question has verifiable, objective ground-truth answers, allowing hard questions to be scored accurately and automatically, without the use of an LLM judge."})}),(0,Ue.jsx)("li",{className:"c18 c29 li-bullet-0",children:(0,Ue.jsx)("span",{className:"c0",children:"LiveBench currently contains a set of 17 diverse tasks across 6 categories, and we will release new, harder tasks over time."})})]}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsx)("span",{className:"c0",children:"Today, we release the initial batch of 960 questions and plan to release sets of questions every month. By doing this, we aim for LiveBench to be free from contamination, since every release will have novel questions."})}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsxs)("span",{className:"c9",children:["In addition to contamination, LiveBench avoids the pitfalls of LLM judging by including only questions that have an objective answer. While LLM judging and crowdsourced prompts and evaluations have many benefits, they also introduce significant biases and completely break down when judging the answers to hard questions. For example, we show in ",(0,Ue.jsx)("a",{href:"https://arxiv.org/abs/2406.19314",target:"_blank",rel:"noreferrer",children:"our paper"})," that for challenging reasoning and math problems, the pass/fail judgments from GPT-4-Turbo have an error rate of up to 46%."]})}),(0,Ue.jsx)("div",{className:"has-text-centered",children:(0,Ue.jsx)("img",{src:fn,alt:"Radial Plot Visualization",style:{maxWidth:"100%",height:"auto"}})}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsx)("span",{className:"c0",children:"LiveBench currently evaluates several prominent closed-source models and dozens of open-weight models ranging from 0.5B to 70B in size. LiveBench questions are difficult; for example, GPT-4-Turbo achieves around 50% accuracy on LiveBench overall. Furthermore, in our monthly updates, we will release new tasks and harder versions of tasks over time so that LiveBench can distinguish between the capabilities of LLMs as they improve in the future."})}),(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsxs)("span",{className:"c0",children:["We release all ",(0,Ue.jsx)("a",{href:"https://huggingface.co/livebench",target:"_blank",rel:"noreferrer",children:"questions"}),", ",(0,Ue.jsx)("a",{href:"https://github.com/livebench/livebench",target:"_blank",rel:"noreferrer",children:"code"}),", and model answers, and we welcome community engagement and collaboration for expanding the benchmark tasks and models."]})})]})]})})})}),(0,Ue.jsx)("section",{className:"section",children:(0,Ue.jsx)("div",{className:"container is-max-desktop",children:(0,Ue.jsx)("div",{className:"columns is-centered has-text-centered",children:(0,Ue.jsxs)("div",{className:"column is-four-fifths",children:[(0,Ue.jsx)("h2",{className:"title is-3",children:"LiveBench Overview"}),(0,Ue.jsxs)("div",{className:"content has-text-justified",children:[(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsx)("span",{className:"c0",children:"LiveBench currently consists of 17 tasks across 6 categories: reasoning, data analysis, math, coding, language comprehension, and instruction following. Each task falls into one of two types: "})}),(0,Ue.jsxs)("ol",{className:"c28 lst-kix_st344of4rgcu-0 start",start:"1",children:[(0,Ue.jsx)("li",{className:"c18 c29 li-bullet-0",children:(0,Ue.jsx)("span",{className:"c0",children:"tasks which use a continuously updated information source for their questions, e.g., data analysis based on recent Kaggle datasets, or fixing typos in recent arXiv abstracts "})}),(0,Ue.jsx)("li",{className:"c18 c29 li-bullet-0",children:(0,Ue.jsx)("span",{className:"c0",children:"tasks which are more challenging or diverse versions of existing benchmark tasks, e.g., from AMPS, Big-Bench Hard, IFEval, or bAbI "})})]}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsx)("span",{className:"c9",children:"The categories and tasks included in LiveBench are:"})}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c4",children:[(0,Ue.jsx)("strong",{children:"Math"}),(0,Ue.jsx)("span",{className:"c9",children:": questions from high school math competitions from the past 12 months (AMC12, AIME, USAMO, IMO, SMC), as well as harder versions of "}),(0,Ue.jsx)("span",{className:"c12",children:(0,Ue.jsx)("a",{className:"c14",href:"https://arxiv.org/pdf/2103.03874",children:"AMPS"})}),(0,Ue.jsx)("span",{className:"c0",children:"\xa0questions"})]}),(0,Ue.jsx)("p",{className:"c4 c15",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c4",children:[(0,Ue.jsx)("strong",{children:"Coding"}),(0,Ue.jsx)("span",{className:"c9",children:": two tasks from Leetcode and AtCoder (via "}),(0,Ue.jsx)("span",{className:"c12",children:(0,Ue.jsx)("a",{className:"c14",href:"https://livecodebench.github.io",children:"LiveCodeBench"})}),(0,Ue.jsx)("span",{className:"c0",children:"): code generation and a novel code completion task"})]}),(0,Ue.jsx)("p",{className:"c4 c15",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c4",children:[(0,Ue.jsx)("strong",{children:"Reasoning"}),(0,Ue.jsx)("span",{className:"c9",children:": a harder version of Web of Lies from "}),(0,Ue.jsx)("span",{className:"c12",children:(0,Ue.jsx)("a",{className:"c14",href:"https://arxiv.org/abs/2206.04615",children:"Big-Bench Hard"})}),(0,Ue.jsx)("span",{className:"c9",children:", and "}),(0,Ue.jsx)("span",{className:"c12",children:(0,Ue.jsx)("a",{className:"c14",href:"https://en.wikipedia.org/wiki/Zebra_Puzzle",children:"Zebra Puzzles"})})]}),(0,Ue.jsx)("p",{className:"c4 c15",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c4",children:[(0,Ue.jsx)("strong",{children:"Language Comprehension"}),(0,Ue.jsx)("span",{className:"c9",children:": three tasks featuring "}),(0,Ue.jsx)("span",{className:"c12",children:(0,Ue.jsx)("a",{className:"c14",href:"https://www.nytimes.com/games/connections",children:"Connections"})}),(0,Ue.jsx)("span",{className:"c0",children:"\xa0word puzzles, a typo removal task, and a movie synopsis unscrambling task from recent movies on IMDb and Wikipedia"})]}),(0,Ue.jsx)("p",{className:"c4 c15",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c4",children:[(0,Ue.jsx)("strong",{children:"Instruction Following"}),(0,Ue.jsx)("span",{className:"c9",children:": four tasks to paraphrase, simplify, summarize, or generate stories about recent new articles from The Guardian, subject to one or more instructions such as word limits or incorporating specific elements in the response"})]}),(0,Ue.jsx)("p",{className:"c4 c15",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c4",children:[(0,Ue.jsx)("strong",{children:"Data Analysis"}),(0,Ue.jsx)("span",{className:"c0",children:": three tasks, all of which use recent datasets from Kaggle and Socrata: table reformatting (among JSON, JSONL, Markdown, CSV, TSV, and HTML), predicting which columns can be used to join two tables, and predicting the correct type annotation of a data column"})]})]})]})})})}),(0,Ue.jsx)("section",{className:"section",children:(0,Ue.jsx)("div",{className:"container is-max-desktop",children:(0,Ue.jsx)("div",{className:"columns is-centered has-text-centered",children:(0,Ue.jsxs)("div",{className:"column is-four-fifths",children:[(0,Ue.jsx)("h2",{className:"title is-3",children:"Motivation"}),(0,Ue.jsxs)("div",{className:"content has-text-justified",children:[(0,Ue.jsxs)("p",{className:"c18",children:[(0,Ue.jsx)("span",{className:"c9",children:"The goal in creating LiveBench is to ensure that its questions are not prone to "}),(0,Ue.jsx)("strong",{children:"contamination"}),(0,Ue.jsx)("span",{className:"c9",children:"\xa0and are "}),(0,Ue.jsx)("strong",{children:"easily, accurately, and fairly evaluated"}),(0,Ue.jsx)("span",{className:"c0",children:"."})]}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsx)("p",{className:"c18",children:(0,Ue.jsx)("span",{className:"c0",children:"Many LLM benchmarks are easily contaminated, since modern LLMs include large swaths of the internet in their training data. This is a problem for LLM evaluations since an LLM\u2019s performance on a benchmark will be artificially inflated, or contaminated, if the LLM has seen the questions of a benchmark during training. \xa0"})}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c18",children:[(0,Ue.jsx)("span",{className:"c9",children:"For example, "}),(0,Ue.jsx)("span",{className:"c12",children:(0,Ue.jsx)("a",{className:"c14",href:"https://arxiv.org/abs/2310.10628",target:"_blank",rel:"noreferrer",children:"recent"})}),(0,Ue.jsx)("span",{className:"c9",children:"\xa0"}),(0,Ue.jsx)("span",{className:"c12",children:(0,Ue.jsx)("a",{className:"c14",href:"http://livecodebench.github.io",target:"_blank",rel:"noreferrer",children:"work"})}),(0,Ue.jsxs)("span",{className:"c9",children:["\xa0shows that LLMs' performance on Codeforces plummets after the training data cutoff date of the LLM, and before the cutoff date, performance is highly correlated with the number of times the problem appears on GitHub. Similarly, a ",(0,Ue.jsx)("a",{className:"c14",href:"https://arxiv.org/pdf/2405.00332",target:"_blank",rel:"noreferrer",children:"recent hand-crafted variant"})," of the established math dataset, GSM8K, shows evidence that several models have overfitted to this benchmark."]})]}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c18",children:[(0,Ue.jsx)("span",{className:"c9",children:"It is also important to ensure that the way we "}),(0,Ue.jsx)("span",{className:"c9 c34",children:"evaluate"}),(0,Ue.jsx)("span",{className:"c0",children:"\xa0an LLM\u2019s answer is fair and free from bias. In contamination-free benchmarks, there are two main approaches that benchmarks have taken: LLM-as-a-judge and Humans-as-a-judge."})]}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c18",children:[(0,Ue.jsx)("strong",{children:"LLM-as-a-judge"}),(0,Ue.jsxs)("span",{className:"c9",children:[": LLM judging is fast and relatively cheap. Furthermore, its biggest strength is its ability to judge open-ended questions, instruction-following questions, and chat bots. However, LLM judging has some important shortcomings. (1) LLMs have biases towards their own answers. Typically only GPT-4 and Claude-3-Opus are used as judges, as the highest-performing LLMs. Yet, GPT-4 and Claude-3-Opus both ",(0,Ue.jsx)("a",{className:"c14",href:"https://lmsys.org/blog/2024-04-19-arena-hard/",target:"_blank",rel:"noreferrer",children:"favor their own answers"}),". (2) They also have a noticeable difference in terms of variance and favorability of other models, and GPT-4 has variance in its own judging, even with temperature 0. (3) For questions that have ground truth answers, LLM judges can make mistakes. For example, question 2 in Arena-Hard asks to write a C++ program to compute whether a given string can be converted to \u2018abc\u2019 by swapping two letters. GPT-4 incorrectly judges its own solution as incorrect. We present more evidence of this phenomenon below."]})]}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c18",children:[(0,Ue.jsx)("strong",{children:"Human-as-a-judge"}),": While human evaluation is great for capturing the preferences of a crowd, using a human-as-a-judge has many disadvantages: (1) human-judging is quite labor-intensive, especially for certain types of questions such as complex math integrals, coding problems, or long-context reasoning problems. (2) For these types of questions, it is also common for humans to make mistakes. (3) There can also be high variability from human to human. And finally, (4) humans also evaluate on metrics other than correctness \u2013 for example, they may prefer outputs of a particular length, \xa0with a particular formatting tone, and formality."]}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsx)("p",{className:"c18",children:"In contrast to these approaches, LiveBench employs objective, ground truth judgements to evaluate each question."}),(0,Ue.jsx)("p",{className:"c10",children:(0,Ue.jsx)("span",{className:"c0"})}),(0,Ue.jsxs)("p",{className:"c18",children:[(0,Ue.jsx)("strong",{children:"Objective ground-truth judging"}),(0,Ue.jsx)("span",{className:"c0",children:'\xa0compares the output of the LLM to a predetermined ground-truth answer. This approach is great because it is trivial to score the outputs in terms of time and cost. Furthermore, it avoids the above mentioned weaknesses in terms of biases, errors, and variability in judging. One weakness is that some types of questions do not have ground-truth answers, such as "write a travel guide to Hawaii.". However, while this limits the type of questions that can be evaluated, it does not affect the validity of evaluation for the questions that can be judged in this manner.'})]}),(0,Ue.jsx)("div",{className:"table-container",children:(0,Ue.jsxs)("table",{className:"table is-bordered is-striped is-narrow is-hoverable is-fullwidth",children:[(0,Ue.jsx)("caption",{className:"has-text-weight-bold",children:"Error rate of LLM-as-a-judge scoring on challenging math (AMC, AIME, SMC) and reasoning (Zebra puzzles) tasks. The judge is GPT-4-Turbo, and it judges model outputs from GPT-4-Turbo and Claude-3-Opus. On all tasks, the error rate is surprisingly high, showing that LLMs are not reliable judges for these tasks"}),(0,Ue.jsx)("thead",{children:(0,Ue.jsxs)("tr",{children:[(0,Ue.jsx)("th",{}),(0,Ue.jsx)("th",{children:"AMC12 2024"}),(0,Ue.jsx)("th",{children:"AIME 2024"}),(0,Ue.jsx)("th",{children:"SMC 2023"}),(0,Ue.jsx)("th",{children:"Zebra Puzzles"})]})}),(0,Ue.jsxs)("tbody",{children:[(0,Ue.jsxs)("tr",{children:[(0,Ue.jsx)("td",{children:"GPT-4-Turbo"}),(0,Ue.jsx)("td",{children:"0.380"}),(0,Ue.jsx)("td",{children:"0.214"}),(0,Ue.jsx)("td",{children:"0.353"}),(0,Ue.jsx)("td",{children:"0.420"})]}),(0,Ue.jsxs)("tr",{children:[(0,Ue.jsx)("td",{children:"Claude-3-Opus"}),(0,Ue.jsx)("td",{children:"0.388"}),(0,Ue.jsx)("td",{children:"0.103"}),(0,Ue.jsx)("td",{children:"0.294"}),(0,Ue.jsx)("td",{children:"0.460"})]})]})]})}),(0,Ue.jsx)("p",{className:"c5",children:(0,Ue.jsx)("span",{className:"c0 c21",children:"Here, we can see that using objective ground-truth judging is superior to LLM judges, particularly on our tasks. On these four tasks, while objective ground-truth judging is perfect, we find that the error rates of LLM judging are far above a reasonable value, indicating that LLM judges are not appropriate for challenging math and logic tasks."})})]})]})})})}),(0,Ue.jsx)("section",{className:"section",children:(0,Ue.jsx)("div",{className:"container is-max-desktop",children:(0,Ue.jsx)("div",{className:"columns is-centered has-text-centered",children:(0,Ue.jsxs)("div",{className:"column is-four-fifths",children:[(0,Ue.jsx)("h2",{className:"title is-3",children:"Comparison to Other Benchmarks"}),(0,Ue.jsxs)("div",{className:"content has-text-justified",children:["We compare our benchmark to current prominent LLM benchmarks: ",(0,Ue.jsx)("a",{href:"https://chat.lmsys.org/?leaderboard",target:"_blank",rel:"noreferrer",children:"ChatBot Arena"})," and ",(0,Ue.jsx)("a",{href:"https://lmsys.org/blog/2024-04-19-arena-hard/",target:"_blank",rel:"noreferrer",children:"Arena-Hard"}),". We see that while there are generally similar trends, some models are noticeably stronger on one benchmark vs. the other, potentially indicating some downsides of LLM judging.",(0,Ue.jsx)("div",{className:"columns is-centered is-vcentered",children:(0,Ue.jsx)("div",{className:"column",children:(0,Ue.jsxs)("figure",{className:"image",children:[(0,Ue.jsx)("img",{src:mn,alt:"Bar plot comparing LiveBench and ChatBot Arena scores across the same models."}),(0,Ue.jsx)("figcaption",{className:"has-text-centered",children:"Bar plot comparing LiveBench and ChatBot Arena scores across the same models."})]})})}),(0,Ue.jsx)("div",{className:"columns is-centered is-vcentered",children:(0,Ue.jsx)("div",{className:"column",children:(0,Ue.jsxs)("figure",{className:"image",children:[(0,Ue.jsx)("img",{src:gn,alt:"Bar plot comparing LiveBench and Arena-Hard scores across the same models."}),(0,Ue.jsxs)("figcaption",{className:"has-text-centered",children:["Bar plot comparing LiveBench and Arena-Hard scores across the same models. Surprisingly, GPT-4 models perform substantially better on Arena-Hard relative to LiveBench, potentially due to the ",(0,Ue.jsx)("a",{href:"https://lmsys.org/blog/2024-04-19-arena-hard/",target:"_blank",rel:"noreferrer",children:"known bias"})," from using GPT-4 itself as the judge."]})]})})}),(0,Ue.jsx)("p",{className:"c5",children:"We find that Pearson's correlation coefficient of model scores between LiveBench and ChatBot Arena is 0.90 and between LiveBench and Arena-Hard is 0.89."}),(0,Ue.jsx)("p",{className:"c5",children:"Based on the plots and the correlation coefficients, we see that LiveBench generally follows similar trends, yet some models are noticeably stronger on one benchmark vs. the other. For example, gpt-4-0125-preview and gpt-4-turbo-2024-04-09 perform substantially better on Arena-Hard compared to LiveBench -- likely due to the known bias from using gpt-4 itself as the LLM judge."}),(0,Ue.jsx)("p",{className:"c26 c15",children:(0,Ue.jsx)("span",{className:"c0 c21"})}),(0,Ue.jsxs)("p",{className:"c26",children:["We are very excited about the future of LiveBench and actively would like to partner with researchers to expand the questions, task list, categories, and models evaluated. If you\u2019d like to contribute or have any questions, ",(0,Ue.jsx)("a",{href:"mailto:samuel@abacus.ai,colin@abacus.ai",children:"please get in touch"}),"."]})]})]})})})}),(0,Ue.jsx)("section",{className:"section",id:"BibTeX",children:(0,Ue.jsxs)("div",{className:"container is-max-desktop content",children:[(0,Ue.jsx)("h2",{className:"title",children:"BibTeX"}),(0,Ue.jsx)("pre",{className:"bibtex",children:(0,Ue.jsx)("code",{children:"\n@inproceedings{livebench,\n  title={LiveBench: A Challenging, Contamination-Free {LLM} Benchmark},\n  author={Colin White and Samuel Dooley and Manley Roberts and Arka Pal and Benjamin Feuer and Siddhartha Jain and Ravid Shwartz-Ziv and Neel Jain and Khalid Saifullah and Sreemanti Dey and Shubh-Agrawal and Sandeep Singh Sandha and Siddartha Venkat Naidu and Chinmay Hegde and Yann LeCun and Tom Goldstein and Willie Neiswanger and Micah Goldblum},\n  booktitle={The Thirteenth International Conference on Learning Representations},\n  year={2025},\n}"})})]})}),(0,Ue.jsx)("footer",{className:"footer",children:(0,Ue.jsxs)("div",{className:"container",children:[(0,Ue.jsxs)("div",{className:"content has-text-centered",children:[(0,Ue.jsx)("a",{className:"icon-link",href:"https://arxiv.org/abs/2406.19314",children:(0,Ue.jsx)("i",{className:"fas fa-file-pdf"})}),(0,Ue.jsx)("a",{className:"icon-link",href:"https://github.com/LiveBench/LiveBench",children:(0,Ue.jsx)("i",{className:"fab fa-github"})})]}),(0,Ue.jsx)("div",{className:"columns is-centered",children:(0,Ue.jsx)("div",{className:"column is-8",children:(0,Ue.jsxs)("div",{className:"content",children:[(0,Ue.jsx)("p",{}),(0,Ue.jsxs)("p",{children:["This website is licensed under a ",(0,Ue.jsx)("a",{rel:"license",href:"http://creativecommons.org/licenses/by-sa/4.0/",children:"Creative Commons Attribution-ShareAlike 4.0 International License"}),".",(0,Ue.jsx)("br",{}),"The site was inspired by the ",(0,Ue.jsx)("a",{href:"https://github.com/nerfies/nerfies.github.io",target:"_blank",rel:"noreferrer",children:"Nerfies project"})," and by ",(0,Ue.jsx)("a",{href:"https://livecodebench.github.io/",target:"_blank",rel:"noreferrer",children:"LiveCodeBench"}),"."]})]})})})]})})]})},Nt=e=>{e&&e instanceof Function&&t.e(453).then(t.bind(t,453)).then((n=>{let{getCLS:t,getFID:a,getFCP:r,getLCP:i,getTTFB:o}=n;t(e),a(e),r(e),i(e),o(e)}))};r.createRoot(document.getElementById("root")).render((0,Ue.jsx)(n.StrictMode,{children:(0,Ue.jsx)(ue,{children:(0,Ue.jsxs)(oe,{children:[(0,Ue.jsx)(re,{path:"/",element:(0,Ue.jsx)(hn,{})}),(0,Ue.jsx)(re,{path:"/insights",element:(0,Ue.jsx)(hn,{})}),(0,Ue.jsx)(re,{path:"/finetunes",element:(0,Ue.jsx)(hn,{})}),(0,Ue.jsx)(re,{path:"/details",element:(0,Ue.jsx)(kt,{})})]})})})),Nt()})()})();
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