Reverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought
Authors: Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, Chong Teng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the CICERO and CICEROv2 datasets validate the significant improvement of our approach on DCMCQ task. |
| Researcher Affiliation | Academia | 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China 2National University of Singapore 3Singapore Management University |
| Pseudocode | No | The paper describes its method steps in text and uses mathematical formulations but does not include a structured pseudocode block or algorithm. |
| Open Source Code | Yes | Codes available at https://github.com/Zheng L00/Re X-Go T |
| Open Datasets | Yes | We assess the efficacy of models on two benchmark datasets, CICERO (Ghosal et al. 2022b) and CICEROv2 (Shen et al. 2022). |
| Dataset Splits | No | The paper describes the datasets used (CICERO and CICEROv2) and evaluation metrics, but it does not explicitly provide details about specific training, validation, or test dataset splits (e.g., percentages or sample counts for each). |
| Hardware Specification | Yes | Our experiments are conducted using NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions using Flan-T5 and GPT3.5 models, but it does not provide specific version numbers for any software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | No | The paper specifies the models used (Flan-T5 variants and GPT3.5) and mentions averaging scores over 5 runs with random seeds, but it does not provide specific details on hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings. |