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If someone did this - it would be nice to collect preference data over answers that are helpful to alignment and not helpful to alignment… that could be a dataset that is interesting for a variety of reasons like analyzing current models abilities to help with alignment, gaps in being helpful w.r.t alignment and of course providing a mechanism for making models better at alignment… a model like this could also maybe work as a specialized type of Constitutional AI to collect feedback from the models preferences that are more “alignment-aware” so to speak… none of this of course is a solution to alignment as the OP points out but interesting nonetheless.

I’d be interested in participating in this project if other folks set something up…

Im struggling to understand how this is is different from “we will build aligned ai to align ai”. specifically: Can someone explain to me how human-like and AGI are different? Can someone explain to me why human-like AI avoids typical x-risk scenarios (given those human-likes could say clone themselves, speed up themselves and rewrite their own software and easily become unbounded)? Why isnt an emulated cognitive system a real cognitive system… i don’t understand how you can emulate a human-like intelligence and it not be the same as fully human-like. 

currently my reading of this is we will build human-like AI because humans are bounded so it will be too, those bounds are: (1) sufffiecent to prevent xrisk (2) helpful for (and maybe even the reason for) alignment. Isnt a big wide open unsolved part of the alignment problem “how do we keep itelligent systems bounded”? What am I missing here?

I guess one maybe supplementary question as well is: how is this different from normal NLP capabilities research which is fundamentally about developing and understanding the limitations of human like intelligence? Most folks in the field say who publish in ACL conferences would explicitly think of this as what they are doing and not trying to build anything more capable than humans.

What are your thoughts on prompt tuning as a mechanism for discovering optimal simulation strategies?

I know you mention condition generation as something to touch on in future posts but I’d be eager to hear about where you think prompt tuning comes in considering continuous prompts are differentiable and so can be learned/optimized for specific simulation behaviour.

Hey there,

I was just wondering how you deal with hallucination and faithfulness issues of large language models from a technical perspective? The user experience perspective seems clear - you can give users control and consent over what Elicit is suggesting and so on.

However we know LLMs are prone to issues of faithfulness and factuality (Pagnoni et al. 2021 as one example for abstractive summarization) and this seems like it would be a big issue for research where factual correctness is very important. In a biomedical scienario, if a user of Elicit gets an output that presents a figure wrongly extracted (say from a proceeding sentence, or hallucinated as the highest log likelihood token based on previous documents), this could potentially have very dangerous consequences. I'd love to know more about how you'd adress that?

My current thinking on the matter is that in order to address these safety issues in NLP for science we may need to provide models that "self-criticize" their outputs so to speak. I.e. provide counterfactual outputs that could be checked or something like this. Espeically since GopherCite (Menick et al 2022) and some of the similar self-supporting models seem to show that self-support is also prone to some issues that doesn't totally address factuality (in their case as measured on TruthfulQA) not to meantion self-explaining approaches which I belive suffer from the same issues (i.e. hallucinating an incorrect explanation).


Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., Chadwick, M., ... & McAleese, N. (2022). Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147.

Pagnoni, Artidoro, Vidhisha Balachandran, and Yulia Tsvetkov. "Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics." arXiv preprint arXiv:2104.13346 (2021).