Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
Authors: Hussein Mozannar, Jimin Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 29,375 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,999 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through user studies on object detection and question-answering tasks, we show that our method can lead to more accurate human-AI teams. We also evaluate our region discovery and description algorithms separately. |
| Researcher Affiliation | Collaboration | 1MIT-IBM Watson AI Lab, Cambridge, MA 2CSAIL and IMES, Massachusetts Institute of Technology, Cambridge, MA 3IBM Research, Cambridge, MA |
| Pseudocode | Yes | Algorithm 1 Integr AI-Describe Input: Dataset D, region Nk |
| Open Source Code | Yes | Code is available in https://github.com/clinicalml/onboarding_human_ai. |
| Open Datasets | Yes | The image datasets include Berkeley Deep Drive (BDD) [83] where the task is to detect the presence of traffic lights in noisy images... and the validation set of MS-COCO (5k) where the task whether a person is present in the image [48]. The text-based validation datasets comprise of Massive Multi-task Language Understanding (MMLU) [33], and Dynamic Sentiment Analysis Dataset (Dyna Sent) [61]. |
| Dataset Splits | No | Each dataset is split into 70-30 ratio for training and testing five different times so as to obtain error bars of predictions. |
| Hardware Specification | Yes | All experiments are run on a Ge Force GTX 1080 Ti. |
| Software Dependencies | No | The paper mentions specific models and libraries used (e.g., 'flan-t5 model', 'ro BERTa-base model', 'sentence transformer', 'CLIP'), but it does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | For our method, we set βu = 0.5, βl = 0.01, α = 0.0 for Aim 1 and βu = 0.1, βl = 0.01, α = 0.5 for Aim 2 and random prior decisions (50-50 for 0 and 1). |