11–20 of 111 posts
This was mostly political guff about environmentalism and bias, but one thing I didn't know was that apparently larger models make it easier to extract training data. > Finally, we note that there are risks associated with the fact that LMs with extremely large numbers of parameters model their training data very closely and can be prompted to output specific information from that training data. For example, [28] dem…
Compare this to something like RLHF[0] which has acheived far more for aligning models toward being polite and non-evil. (This is the technique that helps ChatGPT decline to answer questions like "how to make a bomb?")
There's still a lot of work to be done and the real progress will be made by researchers who implement systems in collaboration with their colleagues and employers.
This paper is embarrassingly bad. It's really just an opinion piece where the authors rant about why they don't like large language models. There is no falsifiable hypothesis to be found in it. I think this paper will age very poorly, as LLMs continue to improve and our ability to guide them (such as with RLHF) improves.
From a technical standpoint, there is little new stuff that I found this paper offered in understanding why LLMs can have unpredictable nature, or what degree of data will get exposed by clever hacks (or if there are systematic ways to go about it). It sounded more like a collection of verifiable anecdotes for easy consumption (which can be a good thing by itself if you want capsule understanding in a non-technical way)
I am of the general understanding that this paper became less about the LLMs & more of a insinuating hit piece against Alphabet. At least, some of the controversial nuggets got Gebru (and later M Mitchell) fired. From a technical standpoint, there is little new stuff that I found this paper offered in understanding why LLMs can have unpredictable nature, or what degree of data will get exposed by clever hacks (or if…
This paper is embarrassingly bad. It's really just an opinion piece where the authors rant about why they don't like large language models. There is no falsifiable hypothesis to be found in it. I think this paper will age very poorly, as LLMs continue to improve and our ability to guide them (such as with RLHF) improves.
You don't come out feeling "Voila! this tiny thing I learnt is something new", which does happen often with many good papers. Most of the paper just felt a bit anecdotal & underwhelming (but I may be too afraid to say the same on Twiiter for good reason)
This paper is the product of a failed model of AI safety, in which dedicated safety advocates act as a public ombudsman with an adversarial relationship with their employer. It's baffling to me why anyone thought that would be sustainable. Compare this to something like RLHF[0] which has acheived far more for aligning models toward being polite and non-evil. (This is the technique that helps ChatGPT decline to answer…
I wonder if anyone's working on public models of this size. Looking forward to when we can selfhost ChatGPT.
This was mostly political guff about environmentalism and bias, but one thing I didn't know was that apparently larger models make it easier to extract training data. > Finally, we note that there are risks associated with the fact that LMs with extremely large numbers of parameters model their training data very closely and can be prompted to output specific information from that training data. For example, [28] dem…
I feel this is a unavoidable consequence of using LLM. We cannot ensure all data is free from any markers. I am not a expert on databases/data engineering so please take it as an informed opinion
This paper is the product of a failed model of AI safety, in which dedicated safety advocates act as a public ombudsman with an adversarial relationship with their employer. It's baffling to me why anyone thought that would be sustainable. Compare this to something like RLHF[0] which has acheived far more for aligning models toward being polite and non-evil. (This is the technique that helps ChatGPT decline to answer…
That's what I didn't like about Gebru - too much critique, not a single constructive suggestion. Especially her Gender Shades paper where she forgot about Asians.
http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a...
I think AnthropicAI is a great company to follow related to actually solving these problems. Look at their "Constitutional AI" paper. They automate and improve on RLHF.
I believe this is the papers that got timnit and mmitchel fired from google, followed by a protracted media/legal campaign against google and vice versa.