4 ms·

Can you share a bit more on which tasks are you discussing about?

• anon373839 2y ago
  Not the OP, but you can take a look at https://spacy.io/usage/spacy-101 https://spacy.io/usage/spacy-101 to get a sense of what traditional NLP tasks look like. These things can be done much faster than LLMs with appropriate tooling (such as spaCy) and they don’t risk hallucination.
  • threeseed 2y ago
    Also you can do it at scale with SparkNLP: https://sparknlp.org https://sparknlp.org Many of the use cases I've seen for LLMs would actually be better with NLP.
    • meandmycode 2y ago
      Which kinds of use cases?
  • maaaaattttt 2y ago
    I’ve tried PII anonymization with standard NLP approaches and LLMs have been (way) better at this task in my experience.
• barrell 2y ago
  In this comment I was referring to POS tagging and feature extraction. Another use case for fine tuning we have as well is to reduce a 5-shot prompt that we have to run hundreds of times per request down to a “0-shot” (heavy emphasis on the double quotes. Run five shot on gpt-4o a couple thousand times, then fine tune on cohere’s command-r or haiku or llama3 8b or whichever small but mighty llm. You can reduce costs by 99%, or somewhere in that ballpark, without really sacrificing quality on 99% of the queries.