[Monday evening short video] Summary of two new amazing LLM benchmarking papers: GAIA and GPQA
TLDRThe discussion introduces two new AI benchmarks, Gia and GP QA, designed to evaluate General AI assistance and superhuman AI capabilities respectively. Gia assesses AI's ability to use the web and tools to answer questions, with varying difficulty levels. GP QA focuses on questions at the edge of human knowledge, crafted by PhD experts. Both benchmarks are open-source, aiming to provide a common ground for comparing and improving AI models.
Takeaways
- 🚀 Introduction of two new AI benchmarks, Gia and GP QA, aimed at evaluating General AI capabilities.
- 🤖 Gia is a benchmark for General AI assistance, focusing on AI's ability to perform tasks like web browsing and information synthesis.
- 🌐 Level one of Gia involves simple web-based queries, such as finding clinical trial data from the NIH website.
- 🌟 Level three of Gia presents more complex tasks, requiring AI to analyze images and perform web searches to answer questions based on them.
- 🔬 GP QA benchmark is designed to test the limits of human knowledge, with questions crafted by PhD holders in various scientific fields.
- 💡 Both benchmarks aim to evaluate AI's reasoning and problem-solving skills, rather than just its memorization capabilities.
- 📈 Success rates for GPT-4 in both benchmarks are around 30%, indicating the complexity and challenge of the tasks.
- 🔍 The questions are carefully designed to be nearly impossible to memorize and not easily found through simple online searches.
- 🔑 The benchmarks are open-source, allowing for community-wide comparison and development of various AI models.
- 🌐 The benchmarks explore a vast combinatorial space of actions, testing AI's ability to navigate and utilize a wide range of tools and resources.
- 🎯 The ultimate goal is to assess and develop AI models that can operate effectively within the full natural space of human knowledge and action.
Q & A
What are the two benchmarks discussed in the transcript?
-The two benchmarks discussed are GIA (General AI assistance benchmark) and GP QA.
What is the purpose of the GIA benchmark?
-The GIA benchmark aims to investigate the capabilities of general AI assistants, testing their ability to use various sources of information, including the web, to answer questions effectively.
What is an example of a Level 1 question in the GIA benchmark?
-An example of a Level 1 question is: 'What was the actual enrollment count of the clinical trial on HPV in acne vulgaris patients from January to May 2018 as listed on the NIH website?'
Why is Level 3 in the GIA benchmark considered very difficult?
-Level 3 is considered very difficult because it requires the AI to not only find specific information but also perform complex reasoning and calculations, such as identifying the astronaut who spent the least amount of time in space from a group involved in a NASA Astronomy Picture of the Day.
How is the GP QA benchmark different from the GIA benchmark?
-The GP QA benchmark is designed to test the limits of human knowledge by using questions crafted by experts in fields like physics, chemistry, or biology, making it extremely challenging for non-experts to answer.
What is the goal of the GP QA benchmark?
-The goal of the GP QA benchmark is to test narrow superhuman AI capabilities, aiming to evaluate AI systems that can surpass human performance in specific tasks, such as advanced problem-solving in scientific domains.
How are the questions in both the GIA and GP QA benchmarks created?
-The questions in both benchmarks are carefully designed by humans, with the GIA questions focusing on general AI assistance and the GP QA questions targeting the limits of human knowledge in specific scientific fields.
What makes these benchmarks valuable for the AI research community?
-These benchmarks are valuable because they are open-source and shared with the community, providing a common ground for comparing various models and building upon existing research to advance AI capabilities.
Why is it important that these benchmarks are not focused on memorization?
-Focusing on memorization is less relevant because the benchmarks aim to test the AI's ability to reason, combine information, and use tools to find answers, rather than just recalling facts.
How do these benchmarks contribute to the future of AI development?
-These benchmarks contribute to the future of AI by pushing the boundaries of AI capabilities, encouraging the development of models that can handle complex tasks and reason at a level that surpasses simple memorization and factual lookup.
What is an example of a complex question from the GP QA benchmark?
-An example of a complex question is related to quantum mechanics, asking for the correct cross-representation of states given a theorization channel operation and the strength of noise, which requires deep expertise to answer.
Outlines
🤖 Introduction to AI Benchmarks
This paragraph introduces two AI benchmarks that were recently published, highlighting their significance in the future of AI evaluation. The first benchmark, GPT, is a collaboration between Fair and other entities, aiming to test the capabilities of General AI assistance. It assesses the AI's ability to perform tasks such as web browsing, image analysis, and information synthesis to answer questions. The benchmark has three levels of difficulty, with level one being relatively easy and level three being extremely challenging for current AI models like GPT Pro. The second benchmark, GP QA, also has a 30% success rate for GPT 4 and is designed to test the limits of human knowledge by posing questions crafted by PhD holders in various scientific fields. The goal is to evaluate AI's ability to reason and use tools to find answers, rather than just memorizing facts. Both benchmarks are open-source and serve as common grounds for comparing and improving various AI models.
🌐 Importance of Reasoning and Knowledge in AI
This paragraph emphasizes the importance of reasoning and knowledge in AI models, rather than just factual recall. It discusses how the benchmarks are designed to explore the vast combinatorial spaces of web exploration and human knowledge, pushing the boundaries of AI capabilities. The focus is on how AI models reason and utilize tools to arrive at answers, rather than on the specific factual details of the answers themselves. The paragraph also highlights the value of these open-source benchmarks in fostering community collaboration and providing a platform for comparing and advancing AI technologies.
Mindmap
Keywords
💡Benchmarks
💡General AI Assistance
💡Gia Benchmark
💡GP QA Benchmark
💡Success Rate
💡Factual Answers
💡Combinatorics of Spaces
💡Open Source
💡Human Knowledge
💡Scalable Oversight
Highlights
Discussion of two new AI benchmarks published recently.
Introduction of the first benchmark, Gia, a General AI assistance benchmark.
Collaboration between Fair, a team at Hugging Face, and O to GPT.
Gia aims to investigate General AI assistants' capabilities.
AI's potential future ability to perform various tasks such as browsing the web and analyzing multimedia.
Explanation of the three levels in the Gaia benchmark, with level three being extremely challenging for current AI.
Level one of Gaia involves finding specific information from the NIH website.
Level three Gaia question example involving NASA's Astronomy Picture of the Day.
GPT Pro's current low success rate at level three Gaia benchmark.
Introduction of the second benchmark, GP QA, with a similar success rate as Gia.
GP QA is designed to test the limits of human knowledge with questions crafted by PhD holders.
The importance of having factually true answers in both benchmarks.
The goal of GP QA to test narrow superhuman AI capabilities.
Challenge of verifying new physics theories created by AI.
The benchmarks' focus on testing models' reasoning and tool usage rather than memorization.
Both benchmarks explore vast combinatorial spaces of actions and knowledge.
The benchmarks are open-source and designed for communal comparison and development.
Excitement around the potential of these new benchmarks for the AI community.