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..
Relevance-Promoting Language Model for Short-Text Conversation
Authors: Xin Li, Piji Li, Wei Bi, Xiaojiang Liu, Wai Lam8253-8260
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a large Chinese STC dataset demonstrate the superiority of the proposed model on relevance metrics and diversity metrics. |
| Researcher Affiliation | Collaboration | Xin Li,1 Piji Li,2 Wei Bi,2 Xiaojiang Liu,2 Wai Lam1 1Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong 2Tencent AI Lab, Shenzhen, China |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://ai.tencent.com/ailab/nlp/dialogue/. |
| Open Datasets | Yes | We utilize the benchmark STC dataset (Liu et al. 2018) to evaluate the effectiveness of the proposed relevance-promoting transformer language model. |
| Dataset Splits | Yes | We split the dataset such that #train:#dev:#test is 7,024,156:2,000:800. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Training details are provided in the appendix. |