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- Collect a bunch of example inputs and outputs from last summer's exploratory work
- We have one or two examples already.
- Wrangle these examples into something we can use to train (fine-tune) a LM
- we could start with the approach of the Interviewer even though it's not perfect...
- Pick an LM that we can feasibly run inference on
- OpenAI API?
- One of the existing open-source ones we've used (maybe an encoder-decoder one like flan-ul2)
- one of the new batch of open-source models (LLaMa etc), fine-tuned perhaps
- Fine-tune the LM to generate questions like our examples
- We have training scripts from 1.5 yr ago for Interview, and last summer for Inquisitive. example
- Alternative: https://arxiv.org/abs/2305.14314, https://github.com/huggingface/peft
- Collect ranking data on LM generations
- build Streamlit app for this? Maybe there's already an app that people are using, e.g., any open-source ChatGPT replication project will have something like this. Vicunia, Alpaca... see llama.cpp repo. or https://arxiv.org/abs/2204.05862
- Use ranking data to optimize the LM
- see https://github.com/eric-mitchell/direct-preference-optimization for one impl
- Deploy the optimized LM as an API that the frontend can access.
- We've already got an API for the interviewer model.
Design a simple format for input and output. e.g., input is document_text, cursor_position, and optional question_type, output is question, start_position, end_position (where positions are character offsets from the beginning of the texts)
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