OpenAI’s involvement in funding and accessing a benchmarking dataset has recently been revealed, raising significant concerns about the authenticity of the high scores achieved by its new o3 AI model. The disclosure has led to increased scrutiny regarding the transparency of the benchmarking process and whether OpenAI’s access to the dataset may have given its model an unfair advantage.
The FrontierMath benchmarking dataset, which has been central to testing AI models, was not only accessed by OpenAI but was also funded by the company. This raises critical questions about the integrity of the dataset and its influence on the performance of the o3 AI model. Given the importance of benchmarking datasets in assessing the capabilities of AI, the undisclosed involvement of OpenAI in funding the creation of FrontierMath has sparked concerns among AI researchers and experts about the potential bias introduced into the results.
In a further twist, the mathematicians who contributed to the development of FrontierMath were unaware that OpenAI was financially backing the project. This lack of transparency has led to calls for greater openness in the creation and use of benchmarking datasets, particularly when they are used to evaluate AI models with significant commercial implications.
The fact that OpenAI’s funding of FrontierMath was only disclosed in the final version of the paper published on Arxiv.org, which formally introduced the benchmark, has raised additional questions about the timing and necessity of such disclosure. Previous versions of the paper had omitted any reference to OpenAI’s involvement, which could suggest that the company’s role was intentionally downplayed until the final version was released. This has led to further criticism of how the results of AI model evaluations are presented to the public and the academic community.
As AI technology continues to evolve, the need for transparency, objectivity, and accountability in benchmarking datasets becomes increasingly critical. The revelation of OpenAI’s hidden funding of FrontierMath highlights the potential for conflicts of interest in AI research and the importance of rigorous standards for ensuring that performance metrics are both fair and reliable.
OpenAI 03 Model Scored Highly On FrontierMath Benchmark
The recent revelations about OpenAI’s secret involvement in the FrontierMath project have raised serious questions about the high scores achieved by the o3 reasoning AI model. Many are now disappointed with the project, as the involvement of OpenAI casts doubt on the integrity of the benchmarking process. In response, Epoch AI has attempted to provide clarity, explaining the situation and outlining their efforts to investigate whether the o3 model was trained using the FrontierMath dataset.
Granting OpenAI access to the FrontierMath dataset was unexpected, particularly because the purpose of the dataset is to test AI models. The core objective of benchmarking is to evaluate AI performance without prior exposure to the questions and answers. If the models are already familiar with the content, the accuracy of the results can no longer be guaranteed.
A post on the r/singularity subreddit voiced frustration over this revelation, quoting a document that claimed the mathematicians behind the FrontierMath dataset were unaware of OpenAI’s involvement. The post highlighted the following concerns:
“Frontier Math, the recent cutting-edge math benchmark, is funded by OpenAI. OpenAI allegedly has access to the problems and solutions. This is disappointing because the benchmark was sold to the public as a means to evaluate frontier models, with support from renowned mathematicians. In reality, Epoch AI is building datasets for OpenAI. They never disclosed any ties with OpenAI before.”
The discussion on Reddit also referenced a publication that shed light on the deeper extent of OpenAI’s involvement. According to this document, the mathematicians creating the problems for FrontierMath were not informed about OpenAI’s funding, which is a significant oversight given the potential conflicts of interest. The document also claimed that OpenAI had access to both the problems and the answers, with the materials allegedly being used for validation purposes, despite Epoch AI’s attempts to downplay this.
Tamay Besiroglu, the associated director at Epoch AI, acknowledged the situation in a statement, confirming that OpenAI had access to the majority of the dataset. However, he clarified that a “holdout” dataset—one that OpenAI did not have access to—was put in place to ensure that the model’s performance could be independently verified. Besiroglu explained:
“Tamay from Epoch AI here. We made a mistake in not being more transparent about OpenAI’s involvement. We were restricted from disclosing the partnership until around the time o3 launched, and in hindsight, we should have negotiated harder for the ability to be transparent with the benchmark contributors as soon as possible. Our contract specifically prevented us from disclosing information about the funding source and the fact that OpenAI has data access to much but not all of the dataset. We own this error and are committed to doing better in the future.”
Besiroglu also addressed concerns about the potential use of the dataset in training the o3 model, assuring the public that OpenAI’s access was limited to certain parts of the dataset. He emphasised that OpenAI had agreed not to use these materials for model training, stating:
“Regarding training usage: We acknowledge that OpenAI does have access to a large fraction of FrontierMath problems and solutions, with the exception of a unseen-by-OpenAI hold-out set that enables us to independently verify model capabilities. However, we have a verbal agreement that these materials will not be used in model training.”
He concluded by reaffirming that OpenAI had been fully supportive of Epoch AI’s decision to maintain the separate, unseen holdout set, which was intended to prevent overfitting and ensure accurate performance evaluation. Besiroglu stressed that FrontierMath was always conceived as a tool for evaluating AI models, and that the arrangements were made with this purpose in mind:
“From day one, FrontierMath was conceived and presented as an evaluation tool, and we believe these arrangements reflect that purpose.”
More Facts About OpenAI & FrontierMath Revealed
Elliot Glazer, the lead mathematician at Epoch AI, has confirmed that OpenAI has access to the FrontierMath dataset. This access allowed OpenAI to evaluate their o3 large language model, which is designed as a reasoning AI model. Glazer shared his belief that the high scores obtained by the o3 model are legitimate, though he emphasised that Epoch AI is conducting an independent evaluation to determine whether or not o3 had used the dataset for training. If it had, this could alter the interpretation of the model’s impressive scores.
In a statement, Glazer said:
“Epoch’s lead mathematician here. Yes, OAI funded this and has the dataset, which allowed them to evaluate o3 in-house. We haven’t yet independently verified their 25% claim. To do so, we’re currently developing a hold-out dataset and will be able to test their model without them having any prior exposure to these problems.”
Glazer also provided his personal opinion on the matter, suggesting that OpenAI’s reported high score is legitimate. He added that he believed OpenAI had no reason to misrepresent internal benchmarking results, stating:
“My personal opinion is that OAI’s score is legit (i.e., they didn’t train on the dataset), and that they have no incentive to lie about internal benchmarking performances. However, we can’t vouch for them until our independent evaluation is complete.”
Furthermore, Glazer clarified that Epoch AI planned to assess o3 using a “holdout” dataset, which OpenAI had not been exposed to. This, according to Glazer, would ensure the integrity of the evaluation:
“We’re going to evaluate o3 with OAI having zero prior exposure to the holdout problems. This will be airtight.”
Glazer also described the process of creating the holdout set in a separate Reddit post, noting that the problems would be chosen randomly from a larger set that would later be added to FrontierMath. He assured that the production process for this holdout set would remain the same as it had been for previous iterations, with no deviation from the established methodology:
“We’ll describe the process more clearly when the holdout set eval is actually done, but we’re choosing the holdout problems at random from a larger set which will be added to FrontierMath. The production process is otherwise identical to how it’s always been.”
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