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Ion Stoica

05/29/25

@ a16z

The challenge lies in estimating how models would perform if infinitely many users asked the same question and voted, which can't be answered through standard regression methods.

Video

Beyond Leaderboards: LMArena’s Mission to Make AI Reliable

@ a16z

Beyond Leaderboards: LMArena’s Mission to Make AI Reliable

05/29/25

Related Takeaways

Amit Jain

05/16/25

@ EO

With large models, you can't predict how users will interact with them, so it's crucial to observe their usage to understand successes and failures.

Marc Andreessen

01/26/25

@ Lex Fridman

The challenge of achieving truth with large language models is a contentious topic in the industry, especially in domains where definitive answers exist, like math and coding.

Amit Jain

05/16/25

@ EO

In AI, if you can do X, users will want to do Y, which creates a challenge because you can't predict how users will interact with your product.

a16z Cast

05/29/25

@ a16z

AI models are valuable because they can interpolate between vague questions and provide tailored responses, even when data is messy.

a16z Cast

05/29/25

@ a16z

AI models are valuable because they can interpolate between vague questions and provide tailored responses, even when data is messy.

Ion Stoica

05/29/25

@ a16z

The 'prompt to leaderboard' approach teaches us that we can evaluate models by learning how they perform in various scenarios, rather than relying solely on static benchmarks.

Sam Altman

06/18/25

@ OpenAI

People are surprisingly willing to wait for a great answer, even if it takes time for the model to think.

Sergey Brin

05/24/25

@ Google for Developers

The ability to allow AI models to reason for extended periods, rather than just providing quick answers, can lead to significantly better outcomes for complex questions.

Anthropic Cast

04/24/25

@ Anthropic

There's a risk of overcomplicating prompts by trying to anticipate every possible issue the model might face, which can lead to ineffective results.