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.
Knowledge Bridging for Empathetic Dialogue Generation
Authors: Qintong Li, Piji Li, Zhaochun Ren, Pengjie Ren, Zhumin Chen10993-11001
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,483 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,844 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 | Extensive experiments conducted on a benchmark dataset verify the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Shandong University, Qingdao, China 2Tencent AI Lab, Shenzhen, China 3Department of Computer Science, The University of Hong Kong, Hong Kong SAR, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Code and dataset are available at http://github.com/qtli/KEMP. |
| Open Datasets | Yes | We conduct our experiments on the EMPATHETICDIALOGUES dataset (Rashkin et al. 2019). |
| Dataset Splits | Yes | Then we obtain 17,802 dialogues in the training set, 2,628 in the validation set, and 2,494 in the testing set. |
| Hardware Specification | Yes | We implemented all models in Py Torch (Paszke et al. 2017) with a single Tesla V100 GPU |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2017)' but does not specify a version number for it or other software dependencies. |
| Experiment Setup | Yes | All common hyperparameters are the same as the work in (Li et al. 2020). The maximum introducing numbers of external concepts per dialogue and per token are set as 10 and 5, respectively. The threshold α used in emotional context graph construction is 0.1. Loss weights γ1, γ2, γ3 are set to 1, 1, and 0.1, respectively. We varied the learning rate during training following Vaswani et al. (2017). Early stopping is applied when training. When inference, we set the maximum decoding step as 30. |