[HN Gopher] Experimenting with LLMs to Research, Reflect, and Plan ___________________________________________________________________ Experimenting with LLMs to Research, Reflect, and Plan Author : gk1 Score : 64 points Date : 2023-04-12 18:53 UTC (4 hours ago) (HTM) web link (eugeneyan.com) (TXT) w3m dump (eugeneyan.com) | tudorw wrote: | I think something akin to a mashup between Engleberts | augmentation, Nelson's Xanadu (r) and Bucky's tensegrity system | would make a great accompanying knowledge management system to | manage branching conversations with AI, after a while handling | the content generated becomes a task in itself. Visualising the | created data would be ace. | tudorw wrote: | 'Sparks of AGI' https://youtu.be/qbIk7-JPB2c | summarity wrote: | > One solution is to ensemble semantic search with keyword | search. BM25 is a solid baseline when we expect at least one | keyword to match. Nonetheless, it doesn't do as well on shorter | queries where there's no keyword overlap with the relevant | documents--in this case, averaged keyword embeddings may perform | better. By combining the best of keyword search and semantic | search, we can improve recall for various types of queries. | | Oh hey I have a demo of that here: https://findsight.ai | | For it I wrote a custom search engine, and KNN index | implementation, which ranks and merges results across three | stages (labels, full-text, embedding) effectively. To speed up | retrieval, OpenAI embeddings are stored instead as SuperBit | signatures. Rank merging turned out to be a really hard problem. | motoboi wrote: | Wait. What!?! This is amazing. Did you write about that? ___________________________________________________________________ (page generated 2023-04-12 23:01 UTC)