Zizhao Chen
@ch272h.bsky.social
chenzizhao.github.io unlearning natural stupidity
So I was volunteering today. I prompted folks randomly this question after they collected their neurips thermos:
Do you think AIs today are intelligent? Answer with yes or no.
Here is the break down:
Yes: 57
No: 62
Total: 119
Pretty close!
Do you think AIs today are intelligent? Answer with yes or no.
Here is the break down:
Yes: 57
No: 62
Total: 119
Pretty close!
December 12, 2024 at 5:00 AM
So I was volunteering today. I prompted folks randomly this question after they collected their neurips thermos:
Do you think AIs today are intelligent? Answer with yes or no.
Here is the break down:
Yes: 57
No: 62
Total: 119
Pretty close!
Do you think AIs today are intelligent? Answer with yes or no.
Here is the break down:
Yes: 57
No: 62
Total: 119
Pretty close!
Extra: search for our wall of shame and fame @cornelltech.bsky.social (trigger alert) (whoa CT has a bsky account?!)
7/7
7/7
November 22, 2024 at 7:21 PM
Extra: search for our wall of shame and fame @cornelltech.bsky.social (trigger alert) (whoa CT has a bsky account?!)
7/7
7/7
We experiment in an abstract multi-turn generalization of reference games. After 6 rounds of grounded continual learning, the human-bot games success rate improves 31→82%📈 - an absolute improvement of 51%, all without any external human annotations! 🚀
4/7
4/7
November 22, 2024 at 7:21 PM
We experiment in an abstract multi-turn generalization of reference games. After 6 rounds of grounded continual learning, the human-bot games success rate improves 31→82%📈 - an absolute improvement of 51%, all without any external human annotations! 🚀
4/7
4/7
How do we decode the reward? Implicit feedback occupies a general and easy to reason about subspace of language
→ Prompt the same LLM that does the task (really bad early on) with a task-independent prompt
→ LLM bootstraps itself
3/7
→ Prompt the same LLM that does the task (really bad early on) with a task-independent prompt
→ LLM bootstraps itself
3/7
November 22, 2024 at 7:21 PM
How do we decode the reward? Implicit feedback occupies a general and easy to reason about subspace of language
→ Prompt the same LLM that does the task (really bad early on) with a task-independent prompt
→ LLM bootstraps itself
3/7
→ Prompt the same LLM that does the task (really bad early on) with a task-independent prompt
→ LLM bootstraps itself
3/7
me: let’s start with a meme
@yoavartzi.com: how about the paper’s fig1? 🙅
me: lesson learned. no memes 😭
A paper on continually learning from naturally occurring interaction signals, such as in the hypothetical conversation above
arxiv.org/abs/2410.13852
1/7
@yoavartzi.com: how about the paper’s fig1? 🙅
me: lesson learned. no memes 😭
A paper on continually learning from naturally occurring interaction signals, such as in the hypothetical conversation above
arxiv.org/abs/2410.13852
1/7
November 22, 2024 at 7:21 PM
me: let’s start with a meme
@yoavartzi.com: how about the paper’s fig1? 🙅
me: lesson learned. no memes 😭
A paper on continually learning from naturally occurring interaction signals, such as in the hypothetical conversation above
arxiv.org/abs/2410.13852
1/7
@yoavartzi.com: how about the paper’s fig1? 🙅
me: lesson learned. no memes 😭
A paper on continually learning from naturally occurring interaction signals, such as in the hypothetical conversation above
arxiv.org/abs/2410.13852
1/7