AttExplainer: Explain Transformer via Attention by Reinforcement Learning
Authors: Runliang Niu, Zhepei Wei, Yan Wang, Qi Wang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three widely used text classification benchmarks validate the effectiveness of the proposed method compared to state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Jilin University 2Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University |
| Pseudocode | Yes | Algorithm 1 DQN training progress |
| Open Source Code | Yes | The code of this paper is available at https://github.com/niuzaisheng/AttExplainer. |
| Open Datasets | Yes | Datasets. We used two types of text classification settings: single sentence classification and Natural Language Inference (NLI). Specifically, single sentence classification datasets include the Emotion dataset [Saravia et al., 2018] and the Stanford Sentiment Treebank (SST2) dataset [Wang and others, 2019]. The NLI dataset we used is the SNLI corpus [Bowman and others, 2015]. The details of these datasets are presented in Table 1. |
| Dataset Splits | No | Table 1 lists '# Train' and '# Test' for each dataset, indicating training and testing splits, but there is no explicit mention of a validation split with specific numbers or percentages for reproducibility. |
| Hardware Specification | Yes | We trained our model on multiple Titan RTX graphics cards. |
| Software Dependencies | No | The paper mentions software like Huggingface, Captum toolkit, and Open Attack, but does not provide specific version numbers for any of these tools or other key software dependencies. |
| Experiment Setup | Yes | The max game step is limited to 100. Max size of replacing buffer for PER is 100,000. The Adam optimizer [Kingma and Ba, 2015] was used for training the DQN model at a fixed learning rate 10^-4, training batch size is 256. We set α = 10, β = 0.2, ϵ = 0.7, γ = 0.9. The number of feature bins b is fixed at 32. The parameters of the DQN target net are replaced by the eval net every 100 learning steps. |