Also of interest is Arbel's paper on GPT-3 (AID currently but he got GPT-3 access recently) legal summaries: https://papers.ssrn.com/sol3/papers.cfm?abstract_id

Also of interest is Arbel's paper on GPT-3 (AID currently but he got GPT-3 access recently) legal summaries: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3740356 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3740356 One thing I would note is the potential for a feedback loop between summaries and the model: if a summary of a specific ToS or piece of law is wrong, you can hardwire an expert-vetted one (there's only so many ToSes or pieces of law and it'll be a long tail), and you can feed back in the correct one as training data to finetune the model. The bigger the GPT model, the smarter it is, and the less feedback it takes to correct its summaries: https://openai.com/blog/learning-to-summarize-with-human-feedback/ https://openai.com/blog/learning-to-summarize-with-human-fee...