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.
MOSS: End-to-End Dialog System Framework with Modular Supervision
Authors: Weixin Liang, Youzhi Tian, Chengcai Chen, Zhou Yu8327-8335
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,459 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,883 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 | Based on our experiments on both Laptop Network and Cam Rest676, we summarize the take-aways for how to ef๏ฌciently build a dataset to solve a task. |
| Researcher Affiliation | Collaboration | 1Zhejiang University, 2University of California, Davis, 3Stanford University, 4Xiaoi Robot Technology Co., Ltd |
| Pseudocode | No | The paper describes the architecture and formulations using mathematical equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the code and data1. 1https://github.com/Youzhi Tian/MOSS-End-to-End-Dialog System-Framework-with-Modular-Supervision |
| Open Datasets | Yes | We release the code and data1. 1https://github.com/Youzhi Tian/MOSS-End-to-End-Dialog System-Framework-with-Modular-Supervision |
| Dataset Splits | Yes | We follow Wen et al. (2017b); Lei et al. (2018) to split the data as 3:1:1 for training, validation and testing. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming language versions or library versions. |
| Experiment Setup | No | The paper describes the model architecture and formulations but does not specify concrete experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |