Post
Shocking: 2/3 of LLMs fail at 2K context length
code_your_own_ai makes a great vlog about mostly LLM related AI content.
As I watched the video below, I wondered about current best practices on LLM evaluation. We have benchmarks, we have sota LLMs evaluating LLMs, we have tools evaluating based on human comparison.
Often, I hear, just play with the LLM for 15 mins to form an opinion.
While I think for a specific use case and clear expectations, this could yield signal carrying experiences, I also see that one prompt is used to judge models.
While benchmarks have their weaknesses, and are by themselves not enough to judge model quality, I still think systematic methods that try to reduce various scientifically known errs should be the way forward, even for qualitative estimates.
What do you think? How can we make a public tool for judging models like lmsys/chatbot-arena-leaderboard help to leverage standards known in social science?
https://www.youtube.com/watch?v=mWrivekFZMM
code_your_own_ai makes a great vlog about mostly LLM related AI content.
As I watched the video below, I wondered about current best practices on LLM evaluation. We have benchmarks, we have sota LLMs evaluating LLMs, we have tools evaluating based on human comparison.
Often, I hear, just play with the LLM for 15 mins to form an opinion.
While I think for a specific use case and clear expectations, this could yield signal carrying experiences, I also see that one prompt is used to judge models.
While benchmarks have their weaknesses, and are by themselves not enough to judge model quality, I still think systematic methods that try to reduce various scientifically known errs should be the way forward, even for qualitative estimates.
What do you think? How can we make a public tool for judging models like lmsys/chatbot-arena-leaderboard help to leverage standards known in social science?
https://www.youtube.com/watch?v=mWrivekFZMM
