SPT

SPT: Selective Prompting Tuning for Personalized Conversations with LLMs

Repo for Selective Prompting Tuning for Personalized Conversations with LLMs, the paper is available at: Selective Prompting Tuning for Personalized Conversations with LLMs

Introduction

In conversational AI, personalizing dialogues with persona profiles and contextual understanding is essential. Despite large language models' (LLMs) improved response coherence, effective persona integration remains a challenge. In this work, we first study two common approaches for personalizing LLMs: textual prompting and direct fine-tuning. We observed that textual prompting often struggles to yield responses that are similar to the ground truths in datasets, while direct fine-tuning tends to produce repetitive or overly generic replies. To alleviate those issues, we propose Selective Prompt Tuning (SPT), which softly prompts LLMs for personalized conversations in a selective way. Concretely, SPT initializes a set of soft prompts and uses a trainable dense retriever to adaptively select suitable soft prompts for LLMs according to different input contexts, where the prompt retriever is dynamically updated through feedback from the LLMs. Additionally, we propose context-prompt contrastive learning and prompt fusion learning to encourage the SPT to enhance the diversity of personalized conversations. Experiments on the CONVAI2 dataset demonstrate that SPT significantly enhances response diversity by up to 90%, along with improvements in other critical performance indicators. Those results highlight the efficacy of SPT in fostering engaging and personalized dialogue generation. The SPT model code is publicly available for further exploration.

Architecture

Experimental Results

Repo Details

Basic Project Structure

• config: contains all the configuration yml file from OPT-125M to Llama2-13B
• data_file: contains CONVAI2 dataset files, dataset can be donwloaded in this Huggingface Repo
• dataset: contains dataloader class and the pre-process methods
• models: contains SPT model classes
• trainer: contains trainer classes, responsible for model training & updating
• utils: provides helper classes and functions
• test.py: the entrance script for model decoding
• train.py: the entrance script for model training

Checkpoint downloading

• The trained checkpoint is located in public_ckpt from Huggingface Repo

Environment Initialization

Modifying env.yml

Since Deepspeed requires the CuDNN and CUDA, and we integrated Nvidia related tools in Anancoda, so it is essential to modify env.yml's instance variable in the last two lines as:

variables:
  LD_LIBRARY_PATH: <CONDA_PATH>/envs/SPT/lib
  LIBRARY_PATH: <CONDA_PATH>/envs/SPT/lib

Please replace <CONDA_PATH> to your own actual conda installation path before importing the env.yml to your environment.

Environment Creation

The SPT's environment can be built using Anaconda (which we recommend), we provide the env.yml for environment creation:

conda env create -f env.yml

conda activate SPT

Model Training

Using following command to start training:

deepspeed --num_nodes=1 train.py \
--config=config/convai2/opt-125m-selective-linear-both-prompt-causal-convai2.yml \
--batch=2 \
--lr=0.0001 \
--epoch=1 \
--save_model=yes \
--num_workers=0 \
--training_ratio=1.0 \
--log_dir=runs_ds_dev \
--deepspeed \
--deepspeed_config ds_config.json

You can adjust --num_nodes if you have multiple GPUs in one node

Main Arguments

• config: the training configuration file
• batch: the batch size per GPU
• lr: learning rate
• epoch: epoch number
• save_model: whether to save model
• training_ratio: the percentage of data used for training, 1.0 means 100%
• log_dir: the log and model save directory
• deepspeed & --deepspeed_config: the necessary arguments for initialize deepspeed
• selective_loss_weight: weight for selection loss
• contrastive_weight: weight for contrastive loss

Model Inference

Model inference can be easily invoked by using the following command:

deepspeed test.py \
--model_path=public_ckpt/OPT-125M-SPT \
--batch_size=16 \
--skip_exists=no \
--deepspeed \
--deepspeed_config ds_config.json

Main Arguments

• model_path: the path to the checkpoint, containing the ds_ckpt folder
• skip_exists: whether to skip decoding if evaluation_result.txt exists

Computing Metrics for Generation Results

To compute the metric for the evaluation results, simply run:

python evaluate_runs_results.py

The input path can be changed in the script via:

_main_path = 'public_ckpt'

Interactive Testing

Also, we support interactive testing via:

deepspeed interactive_test.py \
--model_path=public_ckpt/Llama2-7B-SPT \
--batch_size=1 \
--deepspeed \
--deepspeed_config ds_config.json

So an interactive interface will be invoked as:

Some shortcut keys:

• exit: exiting the interactive shell
• clear: clear the current dialog history
• r: reload SPT's persona

Citation

Will be available soon.