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LLMs can absolutely learn new words. Try it with GPT4, you can make up words with definitions and it will start using them in conversation.

by valine 3y ago

danielmarkbruce 3y ago
Learn implies change, but it's stateless. What you are seeing a nice trick from the chatGPT application where they are keeping the history of your conversation (not in it's entirety, but a sort of summarized version), and in a single turn feeding it all back in. In a single turn it can be told to use word x to mean y and generate output, but I don't think that's "learning".
- valine 3y ago
  It’s called in-context learning. I wouldn’t call it a trick so much as the most exciting and useful feature of LLMs. A lot of research is being done to expand the context window which would allow you to prime the model with entire books or codebases. GPT4 has a max context window of 32k tokens up from 2k with GPT3.5. Whose to say iteratively shifting the output distribution isn’t a form of learning.
  - danielmarkbruce 3y ago
    If you want to call in-context learning learning, that's fine. I thought the presented paper was about the model being updated so that it could be used in "fresh contexts", but I might have misunderstood what they meant by "fresh context". I thought they meant next prompt. I think everyone is aware that if you say "I use the word jimbo to mean dog. Write a story about dogs using my way" it will work. https://chat.openai.com/share/617ef2c3-ad3e-4d1f-8e6b-3940f28b1d2c https://chat.openai.com/share/617ef2c3-ad3e-4d1f-8e6b-3940f2...
    - valine 3y ago
      The model wasn’t update after the initial training, they’re definitely using in-context learning. The advancement here, as I understand it, is that they’ve achieved in-context learning with a very small model. From the paper: >> Few-shot instruction-learning task. MLC was evaluated on this task in several ways; in each case, MLC responded to this novel task through learned memory-based strategies, as its weights were frozen and not updated further.
      - danielmarkbruce 3y ago
        Yup, my mistake

LLMs can absolutely learn new words. Try it with GPT4, you can make up words with definitions and it will start using them in conversation.

by valine 3y ago

LLMs can absolutely learn new words. Try it with GPT4, you can make up words with definitions and it will start using them in conversation.

danielmarkbruce 3y ago

Learn implies change, but it's stateless. What you are seeing a nice trick from the chatGPT application where they are keeping the history of your conversation (not in it's entirety, but a sort of summarized version), and in a single turn feeding it all back in. In a single turn it can be told to use word x to mean y and generate output, but I don't think that's "learning".

valine 3y ago
It’s called in-context learning. I wouldn’t call it a trick so much as the most exciting and useful feature of LLMs. A lot of research is being done to expand the context window which would allow you to prime the model with entire books or codebases. GPT4 has a max context window of 32k tokens up from 2k with GPT3.5. Whose to say iteratively shifting the output distribution isn’t a form of learning.
- danielmarkbruce 3y ago
  If you want to call in-context learning learning, that's fine. I thought the presented paper was about the model being updated so that it could be used in "fresh contexts", but I might have misunderstood what they meant by "fresh context". I thought they meant next prompt. I think everyone is aware that if you say "I use the word jimbo to mean dog. Write a story about dogs using my way" it will work. https://chat.openai.com/share/617ef2c3-ad3e-4d1f-8e6b-3940f28b1d2c https://chat.openai.com/share/617ef2c3-ad3e-4d1f-8e6b-3940f2...
  - valine 3y ago
    The model wasn’t update after the initial training, they’re definitely using in-context learning. The advancement here, as I understand it, is that they’ve achieved in-context learning with a very small model. From the paper: >> Few-shot instruction-learning task. MLC was evaluated on this task in several ways; in each case, MLC responded to this novel task through learned memory-based strategies, as its weights were frozen and not updated further.
    - danielmarkbruce 3y ago
      Yup, my mistake