Graduate-Level Google-Proof Q&A (GPQA) Benchmark

A Graduate-Level Google-Proof Q&A (GPQA) Benchmark is a Q&A benchmark composed of GPQA questions.

• Context:
  • It can (often) assess the robustness of a NLP Model against different types of questions, topics, and difficulty levels, providing insights into its versatility.
  • It can include Out-ot-Domain Adversarial Questions.
  • It can aid in comparing different QA Model architectures, helping researchers identify the most effective approaches for specific applications.
  • ...
• Example(s):
  • the one at https://github.com/idavidrein/gpqa.
  • ...
• Counter-Example(s):
  • GSM1k, GSM8k.
  • Commonsense QA.
  • MMMU Benchmark.
• See: Question-Answering System, Benchmark Dataset, NLP Model, Language Model, QA Model.

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References

2024

• Perplexity
  • The GPQA (Graduate-Level Google-Proof Q&A) Benchmark is a challenging dataset of 448 multiple-choice questions crafted by domain experts in biology, physics, and chemistry to evaluate the capabilities of large language models (LLMs) and scalable oversight mechanisms. The key features of GPQA are:
  • Expert-Level Difficulty: The questions are designed to be extremely difficult, with experts (those with or pursuing PhDs in the relevant fields) achieving an accuracy of only 65% (74% when discounting clear mistakes). This level of difficulty reflects graduate-level understanding in the respective sciences.
  • Google-Proof Nature: Highly skilled non-expert validators, despite having unrestricted web access and spending over 30 minutes per question on average, only reached a 34% accuracy rate. This "Google-proof" characteristic underscores the benchmark's resistance to simple lookup or shallow web searches, aiming at deeper understanding and reasoning.
  • Performance of AI Systems: The strongest GPT-4 based baseline model achieved a 39% accuracy on GPQA, highlighting the significant challenge it poses even to state-of-the-art AI systems. This gap between expert human performance and AI capabilities underscores the need for advanced scalable oversight methods.
  • Objectives and Implications: GPQA challenges LLMs with expert-crafted questions in specialized scientific fields, serving as a crucial tool for scalable oversight. It aims to devise ways for human experts to reliably obtain truthful information from AI systems that surpass human capabilities, guiding the development of sophisticated AI systems capable of contributing to scientific advancements.
  • The introduction of GPQA marks a pivotal moment in AI evaluation, addressing the urgent need for models that can process and generate accurate information in complex scientific domains.
  • Citations:

[1] https://klu.ai/glossary/gpqa-eval
[2] https://github.com/idavidrein/gpqa
[3] https://www.reddit.com/r/mlscaling/comments/18409uu/gpqa_a_graduatelevel_googleproof_qa_benchmark/
[4] https://www.semanticscholar.org/paper/GPQA:-A-Graduate-Level-Google-Proof-Q%26A-Benchmark-Rein-Hou/210b0a3d76e93079cc51b03c4115fde545eea966
[5] https://arxiv.org/abs/2311.12022 

2023

• (Rein et al., 2023) ⇒ David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. (2023). “GPQA: A Graduate-Level Google-Proof Q&A Benchmark.” doi:10.48550/arXiv.2311.12022
  • NOTES:
    • The paper presents GPQA, a challenging dataset of 448 multiple-choice questions in biology, physics, and chemistry, written by domain experts to be extremely difficult and "Google-proof."
    • The paper ensures high question quality by requiring experts with or pursuing PhDs in relevant fields to validate the questions, achieving 65% accuracy, whereas non-experts with unrestricted web access achieve only 34% accuracy.
    • The paper highlights the difficulty of the questions for state-of-the-art AI systems, with GPT-4-based models achieving 39% accuracy, emphasizing the need for scalable oversight methods to supervise AI outputs effectively.
    • The paper details a rigorous data collection and validation process involving multiple stages, including expert and non-expert validation, to ensure the objectivity and difficulty of the questions.
    • The paper discusses the creation of two curated subsets of the dataset: the main set (448 questions) and the diamond set (198 questions), filtered based on expert agreement and non-expert difficulty.
    • The paper demonstrates the dataset's applicability to scalable oversight research by providing a realistic testbed for evaluating human supervision of AI systems, particularly in generating truthful information in specialized domains.
    • The paper acknowledges limitations such as the dataset's small size and potential biases due to the sourcing of experts through Upwork, while highlighting the dataset's potential to inform future AI oversight protocols.

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