Benchmark Saturation

Extracted Attributes

• Field

  Artificial Intelligence (AI)

• Definition

  A phenomenon where improvements measured on AI benchmarks become increasingly smaller, often as models reach an upper limit of measurable performance.

• Implication

  Benchmarks may no longer be effective for measuring and steering progress, or can become misleading measures of actual model capabilities.

• Research Focus

  Mapping global dynamics of benchmark creation and saturation.

• Key Dynamics Identified

  Continuous growth, saturation/stagnation, and stagnation followed by growth in 'State-of-the-Art (SOTA) curves' over time.

• Related Issues in Benchmarking

  Benchmark overfitting, increasing centralization of benchmark dataset creation, contamination, and leakage in benchmark results.

Timeline

• Ott, Barbosa-Silva, Blagec, Brauner, and Samwald published 'Mapping global dynamics of benchmark creation and saturation in artificial intelligence' in Nature Communications, characterizing different shapes of 'SOTA curves' over time. (Source: web_search_results)

  2022-11-09

• A 2023 report authored by researchers at Stanford University identified 'performance saturation on traditional benchmarks' as a top ten takeaway, noting that improvements measured on benchmarks are becoming smaller. (Source: web_search_results)

  2023-01-01

Humanity's Last Exam

Humanity's Last Exam (HLE) is a language model benchmark consisting of 2,500 questions across a broad range of subjects. It was created jointly by the Center for AI Safety and Scale AI.

Web Search Results

• State-of-the-Art: The Temporal Order of Benchmarking Culture - PMC

  researchers at Stanford University was “performance saturation on traditional benchmarks” (Maslej et al., 2023, p. 3). By “saturation” they mean that improvements measured on benchmarks are becoming smaller and smaller, often as models reach an upper limit of measurable performance. Instead of an accelerating, open-ended future that breaks irrevocably with the past, this dynamic of saturation evokes a gradual filling up, a sense of pervasiveness characteristic of the experience of presentism [...] What they have started to worry about is how these metrics (and the rankings they make possible) behave over _time_. Researchers have mapped what they term to be “dynamics” of benchmark saturation, characterizing different shapes of “SOTA curves” over time, using temporal language that evokes cyclical biological development: “continuous growth,” “saturation/stagnation,” and “stagnation followed by growth” (Ott et al., 2022, p. 2). And one of the “top ten takeaways” of a 2023 report authored by [...] 41. Ott, S., Barbosa-Silva, A., Blagec, K., Brauner, J., & Samwald, M. (2022). Mapping global dynamics of benchmark creation and saturation in artificial intelligence. _Nature Communications_, _13_(1), 1–11. 10.1038/s41467-022-34591-0 [DOI] [PMC free article] [PubMed] [Google Scholar.%20Mapping%20global%20dynamics%20of%20benchmark%20creation%20and%20saturation%20in%20artificial%20intelligence.%20Nature%20Communications,%2013(1),%201%E2%80%9311.%2010.1038/s41467-022-34591-0)]

• Mapping global dynamics of benchmark creation and ...

  ."). Benchmarks that are nearing or have reached saturation are problematic, since either they cannot be used for measuring and steering progress any longer, or—perhaps even more problematic—they see continued use but become misleading measures: actual progress of model capabilities is not properly reflected, statistical significance of differences in model performance is more difficult to achieve, and remaining progress becomes increasingly driven by over-optimization for [...] First, we found that a significant fraction of benchmarks quickly trends towards stagnation/saturation, and that this effect was especially marked in the recent past. One approach towards extending the useful lifetime of benchmarks could be an increased focus on benchmarks covering a larger number of sub-benchmarks covering different data distributions and task types. An extreme example is the recently released BIG-Bench benchmark (Srivastava et al. 2022), which contains >200 crowdsourced [...] Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI). However, recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing centralization of benchmark dataset creation. To facilitate monitoring of the health of the AI benchmarking ecosystem, we introduce methodologies for creating condensed maps of the global dynamics of benchmark creation and saturation. We curate data

• AI Benchmarks Hit Saturation | Stanford HAI

  So we’re seeing a saturation among these benchmarks – there just isn’t really any improvement to be made. Additionally, while some benchmarks are not hitting the 90% accuracy range, they are beating the human baseline. For example, the Visual Question Answering Challenge tests AI systems with open-ended textual questions about images. This year, the top performing model hit 84.3% accuracy. Human baseline is about 80%. What does that mean for researchers? [...] ### Stay Up To Date Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly. Sign Up For Latest News ###### Navigate ###### Participate # AI Benchmarks Hit Saturation AI continues to surpass human performance; it’s time to reevaluate our tests. [...] A benchmark is essentially a goal for the AI system to hit. It’s a way of defining what you want your tool to do, and then working toward that goal. One example is HAI Co-Director Fei-Fei Li’s ImageNet, a dataset of over 14 million images. Researchers run their image classification algorithms on ImageNet as a way to test their system. The goal is to correctly identify as many of the images as possible. What did the AI Index study find regarding these benchmarks?

• AI Benchmarking Dashboard | Epoch AI

  For the benchmarks that we evaluate ourselves: we started with the GPQA Diamond and MATH Level 5 benchmarks. This is because they were convenient to run, not yet saturated, and frequently used by researchers and practitioners to evaluate models. We then added Mock AIME 2024-2025 since it is a harder benchmark of mathematics problems than MATH Level 5, which is now reaching saturation. We also added FrontierMath, which evaluates models on extremely difficult mathematics problems, as well as [...] There are potential issues with contamination and leakage in benchmark results. Models may have been exposed to similar questions or even the exact benchmark questions during their training, which could artificially inflate their performance. This is particularly important to consider when evaluating MATH Level 5 results, as many models have been fine-tuned on mathematical content that may overlap with the benchmark. [...] The MATH dataset is widely used and reported on by model developers when evaluating their models’ mathematical reasoning capabilities. We selected the Level 5 subset for our evaluations because it’s not yet saturated by current models, allowing us to focus on the most informative and challenging questions without having to run all 5,000 test questions.

• Benchmark Software Testing: Best Practices & Tips - Abstracta

  Here, it is important to emphasize that benchmark testing is a specialized form of performance testing that emphasizes comparison against established standards. This comparison helps evaluate where the software stands concerning industry standards or competitor products. It’s a reference point for assessing quality and efficiency, providing insights into the software’s competitive standing. [...] Looking to optimize your software’s performance? Explore our Performance Testing Services! ## What Is Benchmark Software Testing? Benchmark software testing is a method to evaluate a software application’s performance against predefined standards. It helps us understand how well our software performs under various conditions. This is crucial for identifying performance bottlenecks and boosting our software’s performance to meet industry standards. [...] It is a subtype of performance testing, involving the comparison of our software’s performance against a set of benchmarks. These benchmarks serve as a reference point, allowing us to measure various performance metrics such as response time, throughput, and resource utilization. By performing benchmark testing, you can identify areas for improvement and optimize your software’s performance. ### Objectives of Benchmark Software Testing Here are the key objectives we focus on at Abstracta: