Self-Learning-LLM

Main code in uncertainty, scripts to run are in script

Important files

• wiki_ques_gen.sh- generates both SFT and DPO dataset as well as performing knowledge filtering
• train.sh - SFT and DPO
• test.sh - do eval on the primary test set on Wikipedia
• tgi.sh - setup the model api to do fast-inference (recommended)

Requirements

• run pip install -r requirements.txt
• If using TGI - install by following https://github.com/huggingface/text-generation-inference?tab=readme-ov-file#local-install

Data Generation

wiki_ques_gen.sh does the following:

1. Generate questions using GPT3.5 on the predefined list of wikipedia articles from https://huggingface.co/datasets/wikimedia/wikipedia in titles.py. Both for test and train (step 1 in paper figure)
2. Generate the greedy decoded response, $y_c^*$.
3. Generate the K sampled responses given the document ($Y_c$) as context for consistency filtering and compute the consistency score, $S_L$.
4. Generate K sampled responses without the context for $Y_r$ and compute knowledge score $S_K$.

Training

• train.sh can train using either PEFT or full parameter training , set the use_peft flag. parameters set in configs/training/lora.yaml
• DPO or SFT in configs/training
• If multi_gpu for full parameter, in configs/deepspeed.yaml
• model config is in configs/model

Testing run script/test.sh, the parameters are specified inside.

• Note that you should first generate the response of the baseline model, being the SFT model, $G_{SFT}$. script/test.sh first generates the response and then perform pairwise ranking with the base_response, which is set in the base_path argument.

Faster Inference

• The code uses TGI for for either data generation or testing. The inference is much faster than standard way of loading model and doing batch generation with model.generate.
• The only troublesome part is that the model have to be first loaded, by running tgi.sh and then running the main script. So if we want to do testing with 2 different models, we have to set up first model -> testing -> unset first model and set 2nd -> testing.
• tgi.sh basically sets up the model on your local hardware for you to make API calls (similar to making it via OpenAI). The code is set up to do multi-threading to increase inference speed.

Extra Notes

• To work with other LLMs, you can just change or replicate the config format in configs/model.
• This work could potentionally work with other forms of unstructured knowledge source besides Wikipedia. The main processing code to gather the documents is the get_predefined_topics function in topic_generator.py file. As long as the entries in the data generation contains the field document, it will construct the dataset.