DeLLMa / DeLLMa

Official Implementation of "DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models"

DeLLMa: A Framework for Decision Making Under Uncertainty with Large Language Models

Project Website | Implementation

Setup the environment by first downloading this repoistory and then running:

pip install -e .

Reproduce DeLLMa results by running

bash ./results.sh

Baslines

• Query GPT-4 for baseline methods. [AGENT] can be chosen from {farmer, trader}, and [BASELINE] can be chosen from {zero-shot, cot, self-consistency}

python main.py --agent_name [AGENT] --dellma_mode [BASELINE] --results_path PATH/TO/RESULT

• Evaluate performance of baseline methods.

python evalute_dellma.py --agent_name [AGENT] --pref_enum_mode [BASELINE] --results_path PATH/TO/RESULT

DeLLMa Agents

• Query GPT-4 for DeLLMa-Naive. Here, [SIZE] denotes the total samples size we use for DeLLMa-Naive (i.e. distributed across all actions). We use 50 in our paper.

python main.py --agent_name [AGENT] --dellma_mode rank --sample_size [SIZE] --results_path PATH/TO/RESULT

• Query GPT-4 for DeLLMa-{Pairs, Top1}. Here, [SIZE] denotes the per action sample size. We use 64 for our best performing agent and ablate from 4 to 64 in our ablation studies. [PCT] denotes the proportions shared between minibatches. For farmer, we use 0.25 and for trader we use 0.5. In our ablation studies we study values from 0, 0.25, 0.5, 0.75.

python main.py --agent_name [AGENT] --dellma_mode rank-minibatch --sample_size [SIZE] --overlap_pct [PCT] --results_path PATH/TO/RESULT

• Evaluate DeLLMa agents. [DeLLMa] can be chosen from {rank, rank-minibatch}. [ALPHA] controls the regularization strength when optimizing the Bradley-Terry Model with the ILSR algorithm, [SOFTMAX] governs how we normalize the utility function after the ILSR procedure. Choices from full (apply softmax to the complete utility vector) and action (first group all state-action pairs by action, then softmax). [TEMP] temperature scaling for the softmax.

python evaluate_dellma.py    \
    --agent_name [AGENT]     \
    --pref_enum_mode [DeLLMa]  \
    --sample_size [SIZE]     \
    --overlap_pct [PCT]      \
    --alpha [ALPHA]          \
    --softmax_mode [SOFTMAX] \
    --temperature [TEMP]     \
    --results_path PATH/TO/RESULT