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Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

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This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-ups across 12 LLMs, but it can also potentially improve the answer quality on several question categories. To make the overall solution more practical, an extension, SoT with Router (SoT-R), employs a GPT-4-prompting router or a trained RoBERTa router to only trigger SoT for suitable questions. SoT is an initial attempt at data-centric optimization for inference efficiency, and further underscores the potential of pushing LLMs to think more like a human for answer quality.

If you find this repository or paper useful, you can cite

@inproceedings{
ning2024skeletonofthought,
title={Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation},
author={Xuefei Ning and Zinan Lin and Zixuan Zhou and Zifu Wang and Huazhong Yang and Yu Wang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=mqVgBbNCm9}
}

The repo is organized as follows.

Contents

Install

pip install -e .

We recommend using Python 3.8 to 3.10.

Some required environment variables/setups for using the API-based models:

Test SoT with Gradio Demo

The SoT gradio demo for open-source models can be started by running the following commands under the demo/ directory:

  1. Launch the controller
python controller.py
  1. Launch the model workers
  1. Launch the Gradio web demo
python gradio_web_server_multi.py

Test SoT in the Console

Besides chatting with SoT using the web demo, another convenient way to check how SoT works on specific questions is to use the sot/prompt_eng_main.py helper program. In the interactive session popped by this helper program, one can issue questions saved in data files to SoT and check the outputs in the console conveniently. See this section for more details.

Evaluate SoT

Prepare the dataset

Vicuna-80, WizardLM, and LIMA data are provided under data/ and are ready to use. The pre-processing scripts for getting the data are also attached (create_dataset.py in each folder) for reference.

Dump the answers of SoT and Normal decoding

We put the answer dumping scripts for the Vicuna-80 and WizardLM datasets under scripts/vicuna/dump/ and scripts/wizardlm/dump/.

For example, to dump SoT answers of all open-source models, we can run

python scripts/vicuna/dump/opensource_outline.py

To dump the normal sequential decoding answers of GPT-3.5, we can run

./scripts/vicuna/dump/gpt3.5_naive.sh

Evaluate the answer quality

We put the evaluation scripts for the Vicuna-80 and WizardLM datasets under scripts/vicuna/evaluate/ and scripts/wizardlm/evaluate/.

The evaluation scripts use the comparison prompts provided by Fastchat or LLMZoo to prompt a GPT-4 judge to compare the quality of two answers. Please provide the OpenAI API key by export OPENAI_API_KEY=<API key> before running the scripts.

Note: We did not use the system prompt except for the LLaMA-2 models when conducting open-source model evaluation in our paper (for both normal decoding and SoT decoding). This is because we use an older FastChat version for the evaluation in the paper. As our code removes the template messages in the conversation template before querying the model, the system prompt will be removed as template messages in the old FastChat version. Nevertheless, in this code repository, we use a newer version of FastChat (v0.2.26). Consequently, running SoT with the current code will use the system prompt for all open-source models.

The above evaluation is only for SoT (without routers). Please refer to prompts/router_gpt4.json for the prompt we use for SoT-R with Prompting Router (using GPT-4), and this section for details about SoT-R with Trained Router (using RoBERTa).

Develop SoT

Manually tune the SoT prompts

sot/prompt_eng_main.py is a helper program to ease manual prompt tuning. Use bash scripts/debug_prompt.sh <model name or path> to run the script. This will pop an interactive session in which you can run the following commands:

  1. usedata <data filepath> to load data from the given file path (default: data/vicuna/data.csv)
  2. useprompt <prompt filepath> to change the SoT prompt templates (default: prompts/sot_opensource.json)
  3. usenaiveprompt <prompt filepath> to change the normal prompt template (default to use only the question)
  4. (1) test <ind> to test SoT decoding for the <ind>-th question; (2) test naive <ind> to test normal decoding; (3) test batch_outline <ind> to test SoT decoding with batched point expansion.
    • The model outputs will be streamed onto the console (by enabling --stream argument to sot/prompt_eng_main.py). Note that when using test <ind>, the expansion of multiple points is conducted sequentially. When using test batch_outline <ind>, the expansion of multiple points is conducted with batch inference, but we do not support streaming the parallel expansion outputs to the console (to check the streaming effect, use the Gradio Web Demo), so one has to wait until the point-expanding completion to see the results.
    • After the completion, statistics will also be printed.
    • At any time during the generation, one can push Ctrl+C to abort the generation to go back to the interactive session.
  5. exit to exit the session

Note:

  1. We mainly use this program to help engineer the prompt for the open-source models, and didn't test it with the API-based models.
  2. Any other command-line arguments for the model can be fed as the arguments to this script. For example, as testing a 13B model on RTX 3090 with FP16 inference requires two GPUs, we can run bash scripts/debug_prompt.sh meta-llama/Llama-2-13b-chat-hf --num-gpus 2

Train the router for SoT-R

Preprocess router data and train the RoBERTa router as follows (scripts in sot/train/):

  1. Preprocess the router data for Vicuna-80, WizardLM, and LIMA:
python offline_prepare_router_data.py \
  --data_path "../../data/lima/router.csv" \
  --output_data_path "lima_router.pkl"
  1. Train the router on LIMA and test on Vicuna-80 and WizardLM:
python train_router.py

The predicted results will be saved as vicuna_router_pred.csv and wizardlm_router_pred.csv.

Our trained router model can be found on this Google Drive.

Our manual labels of whether each question should use SoT are provided in data/*/router.csv.

Acknowledgement

During the development of SoT, we use and refer to the amazing work of FastChat and Hugging Face transformer package.