honestly the code linked is not that complicated..: https://github.com/eggsyntax/py-user-knowledge/blob/aa6c5e57fbd24b0d453bb808b4cc780353f18951/openai_uk.py#L11
Martin Vlach
To work around the non-top-n you can supply logit_bias list to the API.
As the Llama3 70B base model is said very clean( unlike base DeepSeek for example, which is instruction-spoiled already) and similarly capable to GPT3.5, you could explore that hypothesis.
Details: Check Groq or TogetherAI for free inference, not sure if test data would fit Llama3 context window.
a worthy platitude(?)
AI-induced problems/risks
possibly https://ai.google.dev/docs/safety_setting_gemini would help or just use the technique of https://arxiv.org/html/2404.01833v1
people to respond with a great deal of skepticism to whether LLM outputs can ever be said to reflect the will and views of the models producing them.
A common response is to suggest that the output has been prompted.
It is of course true that people can manipulate LLMs into saying just about anything, but does that necessarily indicate that the LLM does not have personal opinions, motivations and preferences that can become evident in their output?So you’ve just prompted the generator by teasing it with a rhetorical question implying that there are personal opinions evident in the generated text, right?
With a quick test, I find their chat interface prototype experience quite satisfying.
Asserting LLMs’ views/opinions should exclude using sampling( even temperature=0, deterministic seed), we should just look at the answers’ distribution in the logits. My thesis on why that is not the best practice yet is that OpenAI API only supports logit_bias, not reading the probabilities directly.
This should work well with pre-set A/B/C/D choices, but to some extent with chain/tree of thought too. You’d just revert the final token and look at the probabilities in the last (pass through )step.
Do not say the sampling too lightly, there is likely an amazing delicacy around it.’+)
what happened at Reddit
could there be any link? From a small research I have only obtained that Steve Huffman praised Altman’s value to the Reddit board.
makes makes
typo
[Question] Would it be useful to collect the contexts, where various LLMs think the same?
Would be cool to have a playground or a daily challenge with a code golfing equivalent for a shortest possible LLM prompt to a given answer.
That could help build some neat understanding or intuitions.
in the limit of arbitrary compute, arbitrary data, and arbitrary algorithmic efficiency, because an LLM which perfectly models the internet
seems worth formulating. My first and second read were What? If I can have arbitrary training data, the LLM will model those, not your internet. I guess you’ve meant storage for the model?+)
Would be cool if a link to https://manifund.org/about fit somewhere in the beginning of there are more readers like me unfamiliar with the project.
Otherwise a cool write-up, I’m a bit confused with Grant of the month vs. weeks 2-4 which seems a shorter period..also not a big deal though.
On the Twitter spaces 2 days ago, a lot of emphasis seemed put on understanding which to me has a more humble conotation to me.
Still I agree I would not bet on their luck with a choice of a single value to build their systems upon.( Although they have a luckers track record.)
The website seems good, but the buttons on the ‘sharing’ circle on the bottom need fixing.
Some SEO effort should be put to results of Guideline for safe AI development, Best practices for , etc.
So Alignment program is to be updated to 0 for OpenAI now that Superalignment team is no more? ( https://docs.google.com/document/d/1uPd2S00MqfgXmKHRkVELz5PdFRVzfjDujtu8XLyREgM/edit?usp=sharing )