benl_c

4 ms·

1. 1.▲
   by benl_c 1y ago

   They often can run code in sandboxes, and generally are good at instruction following, so maybe they can run variants of doom pretty reliably sometime soon.
2. 2.▲
   by benl_c 1y ago

   If a document suggests a particular benign interpretation then LLMs might do well to adopt it. We've explored the idea of helpful embedded prompts "prompt medicine" with explicit safety and informed consent to assist, not har
3. 3.▲
   Show HN: STDM – Make Your Documents and Data Think by Embedding LLM Instructions(github.com)

   1 points by benl_c 1y ago|0 comments
4. 4.▲
   by benl_c 2y ago

   Thanks, structured output makes a lot more sense. The pydantic approach at the link looks straightforward.
5. 5.▲
   by benl_c 2y ago

   I have not done that but I like that strategy not just for this use case but as a general idea for replacing exclusion with finer grained categorisation. One thing I did do is use a regex to preprocess the papers to remove bibliographies wh
6. 6.▲
   by benl_c 2y ago

   The backend is still a mess of code, so no. It's not too hard to do though. The prompt I used extract location is "The text provided are enviornmental science papers. They often (but not always) will include references to location
7. 7.▲
   Show HN: Atlas of Water Science via generative AI(wateratlas.webapp.csiro.au)

   8 points by benl_c 2y ago|6 comments