Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
Authors: Jae-Woo Choi, Youngwoo Yoon, Hyobin Ong, Jaehong Kim, Minsu Jang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using the proposed benchmark system, we perform extensive experiments with LLMs and prompts, and explore several enhancements of the baseline planner. |
| Researcher Affiliation | Collaboration | Jae-Woo Choi1 , Youngwoo Yoon1 , Hyobin Ong1,2, Jaehong Kim1, Minsu Jang1,2 1 Electronics and Telecommunications Research Institute 2 University of Science and Technology EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | 4) public release of benchmark code and extended dataset (WAH-NL); they are available at https://github.com/lbaa2022/LLMTask Planning. |
| Open Datasets | Yes | ALFRED dataset (Shridhar et al., 2020) with AI2-THOR simulator (Kolve et al., 2017), and 2) our extension of Watch-And-Help (WAH) dataset (Puig et al., 2021), WAH-NL, paired with Virtual Home simulator (Puig et al., 2018). [...] public release of benchmark code and extended dataset (WAH-NL); they are available at https://github.com/lbaa2022/LLMTask Planning. |
| Dataset Splits | Yes | The ALFRED dataset consists of three sets: train, valid-seen, and valid-unseen. The valid-seen was used to evaluate planning performance; the train set was only used to take examples to construct prompts. |
| Hardware Specification | Yes | Most of the models were run on a single NVIDIA A100 80GB GPU, while we used two A100 GPUs and three RTX 6000 GPUs for inference of larger models, such as OPT 66B and LLa MA 2 70B, with model parallelism. |
| Software Dependencies | No | The paper mentions software like Hugging Face's Transformers library, Open AI's GPT API, and Guidance library, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The default setup is to include six examples in ALFRED and five examples in WAH-NL (one example per task type). [...] We finetuned LLa MA 1 models using Lo RA (Hu et al., 2021)... with the hyper-parameters set as follows: the rank of Lo RA modules was set to 16 (reduced to 8 for the 30B model due to GPU memory constraints), the dropout rate was 0.1, and the number of epochs was 5. |