aepo 0.1.4

Annotation Efficient Preference Optimization

Annotation-Efficient Preference Optimization

This repository implements the Annotation-Efficient Preference Optimization (AEPO) algorithm.

The code is tested on Ubuntu 20.04 using Python 3.9 and CUDA 11.0 (Docker image nvidia/cuda:11.0.3-cudnn8-devel-ubuntu20.04).

Install

You can install aepo via pip.

pip install aepo

Source install is available too. Clone this repository and run pip install ..

git clone [e-mail]:CyberAgentAILab/annotation-efficient-po.git
cd annotation-efficient-po
pip install .

Usage

The command line interface is available. The input dataset can be csv file or a dataset uploaded to Huggingface Hub. The dataset should have a column named prompt or instruction. aepo recognize it as the user prompt given to the system and the rest of the columns to be the responses generated by the system.

I prepared an example dataset in dataset/alpaca_samples.csv. The csv file includes 128 responses generated by HuggingFaceH4/mistral-7b-sft-beta for each instruction of the alpaca_human_preference split of tatsu-lab/alpaca_farm. You can try aepo using this dataset with the following command:

aepo dataset/alpaca_samples.csv --num_responses 8 --num_annotations 2 --num_instructions 10

--num_responses is the number of input responses you use. The dataset has to have responses larger than or equal to --num_responses. --num_annotations is the number of responses after the subsampling process. It is also the number of times the reward model is queried per instruction.

Example: Running AEPO

You can generate a pair of responses for each instruction using aepo using the following command.

aepo dataset/alpaca_samples.csv --num_responses 8 --num_annotations 2 --num_instructions 10

To subsample four responses for e.g., LiPO, set --num_annotations to four.

aepo dataset/alpaca_samples.csv --num_responses 8 --num_annotations 4 --num_instructions 10

Example: Running West-of-N over 8 samples

West-of-N is a strategy to pick the Best-of-N as the chosen response, and Worst-of-N as a rejected response. It is shown to be effective for DPO and reward modeling. You can run West-of-N using this package by setting --num_annotations == --num_responses.

aepo dataset/alpaca_samples.csv --num_responses 8 --num_annotations 8 --num_instructions 10

This command will generate a dataset with 8 responses, ranked by their rewards. If you only need the best and worst of the N samples, then use --west_of_n option.

aepo dataset/alpaca_samples.csv --num_responses 8 --num_annotations 8 --num_instructions 10 --west_of_n

This will pick the best and worst responses as the chosen and rejected. The rest of the responses are discarded. It would be useful to construct a pairwise preference dataset.

Reference

TBA. Yuu Jinnai and Honda Ukyo. Annotation-Efficient Preference Optimization for Language Model Alignment, 2024.

Contact

For any questions, feel free to raise an issue or contact me at [e-mail].