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Soft Self-Consistency Improves Language Model Agents

Code for the paper Soft Self-Consistency Improves Language Model Agents (ACL 2024).

Requirements

You can install required packages by running the following command:

conda create -n softsc python=3.9
conda activate softsc
pip install -r requirements.txt

You may get issues when using bitsandbytes, my suggestion is to execute this command: conda install nvidia::cudatoolkit==11.1.74.

Prepare the Environment for Bash

Please follow the setup in InterCode to install InterCode (recommended to build from source) and create the docker images for the Bash environment.

Attention: InterCode has four bash subsets and each subset has its own docker image. Please make sure to create all four docker images and each docker image should be named as intercode-nl2bash{i}, where i is the subset number. (Tips: you need to modify ENV file_system_version={i} in intercode/docker/nl2bash.Dockerfile to create the corresponding docker image.)

Prepare the Environment for Webshop

Please follow the setup instructions in WebShop to install WebShop (recommended to use flag -d all to load all products) and launch the environment in a separate conda environment.

Prepare the Environment for ALFWorld

Please follow the installation of ALFWorld from source to setup the environment. To use extra functionality used in this work, please replace alfworld/agents/environment/alfred_tw_env.py file with the one provided in ADaPT.

Run Experiments

For Bash

You can run our code following the example below:

python run_bash.py \
    --model codellama/CodeLlama-7b-Instruct-hf \
    --cache_dir /path/to/cache_dir/of/model \
    --load_in_4bit \
    --k 20 \
    --aggregation mean \
    --method SoftSC \
    --output_dir /path/to/output_dir

We explain some import arguments below (some are not shown in the example):

• k: the number of samples.
• aggregation: the aggregation method for the samples. We support mean, min, and prod.
• method: the method to use. We support Self-Consistency SC, Soft Self-Consistency SoftSC, Adaptive Self-Consistency AdaptiveSC, and Adaptive Soft Self-Consistency AdaptiveSoftSC.
• threshold (only for Adaptive methods): the threshold for the Adaptive methods AdaptiveSC and AdaptiveSoftSC.

You also can score the your own bash output file by following the example below:

python score_eval_bash.py \
    --model codellama/CodeLlama-7b-Instruct-hf \
    --cache_dir /path/to/cache_dir/of/model \
    --input_file /path/to/your_own_bash_output_file \
    --load_in_4bit \
    --aggregation mean \
    --method SoftSC \
    --output_dir /path/to/output_dir

Your own bash output file should be in the format like:

{
    "0": {
        "query": "Calculate a list of duplicate md5 sum hashes for all the \".java\" files in the /testbed directory",
        "actions": [
            "find /testbed -name \"*.java\" -exec md5sum {} \\; | awk '{print $1}' | sort | uniq -d",
            "find /testbed -type f -name \"*.java\" -exec md5sum {} \\; | cut -d' ' -f1",
            "find /testbed -type f -name \"*.java\" | xargs md5sum | awk '{print $1}' | sort | uniq -d",
            "find /testbed -type f -name \"*.java\" -exec md5sum {} + | sort | uniq -d -w 32",
        ]
    }
    "1": {
    ...
    }
    ...
}

For Webshop

You can generate 20 trajectories for selecting and buying products for each task instance by running:

python run_webshop.py --k 20 --start 0 --num_sess 50

You also can score your own WebShop output file by following the example below:

python score_eval_webshop.py \
    --model codellama/CodeLlama-7b-Instruct-hf \
    --cache_dir /path/to/cache_dir/of/model \
    --data_path /path/to/your_own_webshop_output_file \
    --load_in_4bit \
    --k 20 \
    --aggregation mean \
    --method SoftSC \
    --output_dir /path/to/output_dir

For ALFWorld

In ALFWorld, we sample multiple actions for each step and select one, therefore, scoring is performed implicitly. To run ALFWorld on the test set, use:

python run_alfworld.py --k 10 --eval-all True \
--score avg --fname test_results --LM llama

For running self-consistency set score as majority.

Acknowledgement

We sincerely thank the authors of InterCode, WebShop, and ALFWorld for their contributions!

Citation

@inproceedings{wang-etal-2024-soft,
    title={Soft Self-Consistency Improves Language Model Agents}, 
    author={Han Wang and Archiki Prasad and Elias Stengel-Eskin and Mohit Bansal},
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    year={2024},
}