Note from @lumpenspace:
This technique is something ive been working last summer/fall, originally planning to get a paper out of it. Then it seemed obvious so i didn't, and instead used pieces of this repo for other projects and abandoned this repo.
I discovered not without horror that some of the tech is still cutting edge, so i might as well share it.
In this old version, the main simulee was Gary Marcus; the idea was to make a model that could pass as him in a conversation and demonstrate how stochastic parrots are still plenty capable to mimic the deterministic ones, but there's a couple interesting tidbits that i've moved to more decent repos but, given my pretty annoying habit of not sharing subpar code, you might as well start here.
Scroll to usage and functionality for cli options, what you can do (apart from what's described below) is automagically fetch, chunk, embed, story, and query a db starting from a substack url.
Not guaranteeing anything works, but it's a good starting point for a lot of things and includes a couple of new ideas.
ok then, a friend asked so now it is more lenient with the version number and uses poetry for the dependencies. It's still a mess, but it's a more runnable mess.
RAFT, or Retrieval-Augmented Fine-Tuning, is a method comprising of a fine-tuning and a RAG-based retrieval phase. It is particularly suited for the creation of agents that realistically emulate a specific human target.
RATF, or Replica Agent Testing Framework, is a framework for evaluating the performance of dialogue agents emulating real-world targets.
The emulation of specific humans in conversational agents presents unique challenges and opportunities for contextual understanding, theory of mind and personalization. In this paper, we introduce the Retrieval-Augmented Fine-Tuning (RAFT) methodology, designed explicitly for simulating individual humans.
RAFT employs a dual-phase process:
In the Retrieval-Augmented Fine-Tuning phase proper, combines interview transcripts featuring the human target with appropriately selected, rephrased and evaluated "memories" from the author's past output to give the model a sense of the way the target human combines past writings with the current context to generate responses.
In the generation phase, these memories augment the language model's responses to create a nuanced and personalized dialogue.
We demonstrate the efficacy of RAFT through a unique evaluation metric, RATF (Replica Agent Testing Framework) that compares model-generated responses with original human responses in an interview setting. Our findings highlight RAFT's potential to significantly advance the field of personalized, context-sensitive conversational agents.
Two datasets are required for the fine-tuning phase:
- A dataset of interview transcripts featuring the target human
- A dataset of the target's past written output (tweets, essays, etc.)
The interview transcripts used within a RAG-inspired process retreiving "memories" from the target's written output for each of the interviewer's questions. These memories are then rephrased and evaluated in the context of the target user's answer and, if found useful, they are interpolated between question and answer for the fine-tuning phase.
The steps to reproduce this process are as follows:
- Create a dataset of interview transcripts featuring the target human. Each interview should be a separate file, with the interviewer's questions and the target human's answers separated by a newline. [TODO: update w current method]
- Create a dataset of the author's past written output.
- Split the past output dataset in chunks of a size suitable for the chosen embedding model (8192 tokens for Openai's text-embedding-ada-002), and collect metadata and embeddings for each chunk.
- Store the resulting metadata and embeddings in a vector database (we use ChromaDB).
Then, in order to generate a fine-tuning dataset:
- For each interview, run the RAG process to retrieve memories from the author's past output for each of the interviewer's questions.
- Ask the model to rephrase each memory in the context of the interviewer's question. The same model and prompt will be used in the generation phase.
- Evaluate the resulting memory by the question only first, and discard it if it is not considered useful by the model. We apply this first pass separately because, at inference time, we will not have access to the target human's answer.
- Save the resulting context including question, memory and as many of the previous [question, memory and answers] tuples as possible, up to the maximum context size the finetune allows, as a new finetune sample.
The fine-tuned model is then used to generate responses to the interviewer's questions. The model is prompted with the question and the rephrased memories, and the resulting response is evaluated using the RATF framework.
use Poetry to install the dependencies and run the script; poetry install
poetry run raft -husage: raft [-h] [--oai] [--generic]
{fetch,chunk,embed,ft:gen,ft:run,bench:setup} name
Run the raft command.
positional arguments:
{fetch,chunk,embed,ft:gen,ft:run,bench:setup}
The action to perform; see below for details.
name The name of the blog to process.
options:
-h, --help show this help message and exit
--oai Only generate finetune or benchmark for openai (from
existing generic file) .
--generic Only generate generic finetune or benchmark file.
The following actions are available:
- fetch: Fetch the blog from Substack and store it in the data directory.
- chunk: Chunk the blog into 4096 token pieces and store them in the data directory.
- embed: Create embeddings for the chunks and store them.
- ft:gen: Generate finetune files for the blog.
- ft:run: Run the finetune job for the blog.
- bench:setup: Setup the benchmark for the blog.public domain, whatevs
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