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When it comes to traditional NLP related tasks, LLMs are far below dedicated NLP pipelines like POS tagging and feature tagging. However, fine tuning bridges th
by barrell 2y ago
When it comes to traditional NLP related tasks, LLMs are far below dedicated NLP pipelines like POS tagging and feature tagging. However, fine tuning bridges the gap quite a bit between the two.
It's a narrow domain, but so is most of programming. I think if you're just training a general purpose LLM to be more inclined towards your data -- no, fine tuning is probably not very relevant. But if you're trying to solve a very specific yet fuzzy problem, and LLMs can get you _part_ of the way there, fine tuning is likely your best bet.
- NhanH 2y agoCan you share a bit more on which tasks are you discussing about?
- anon373839 2y agoNot 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 agoAlso 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 agoWhich kinds of use cases?
- maaaaattttt 2y agoI’ve tried PII anonymization with standard NLP approaches and LLMs have been (way) better at this task in my experience.
- barrell 2y agoIn 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.