Prompt-augmented Temporal Point Process for Streaming Event Sequence
Authors: Siqiao Xue, Yan Wang, Zhixuan Chu, Xiaoming Shi, Caigao JIANG, Hongyan Hao, Gangwei Jiang, Xiaoyun Feng, James Zhang, Jun Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a novel and realistic experimental setup for modeling event streams, where Prompt TPP consistently achieves state-of-the-art performance across three real user behavior datasets. |
| Researcher Affiliation | Industry | Ant Group Hangzhou, China {siqiao.xsq,luli.wy,chuzhixuan.czx}@alibaba-inc.com |
| Pseudocode | Yes | Algorithm 1 Prompt TPP at training time of the T -th task. Algorithm 2 Prompt TPP at test time of the T -th task. |
| Open Source Code | Yes | Our code is available at https://github.com/yanyan Sann/Prompt TPP |
| Open Datasets | Yes | We conduct our real-world experiments on three sequential userbehavior datasets. In each dataset, a sequence is defined as the records pertaining to a single individual. The Taobao (Alibaba, 2018) dataset... The Amazon (Ni, 2018) dataset... The Stack Overflow (Leskovec & Krevl, 2014) dataset... |
| Dataset Splits | Yes | The subset in each task is split into training, validation, and test sets with a 70%, 10%, 20% ratio by chronological order. |
| Hardware Specification | Yes | All the experiments were conducted on a server with 256G RAM, a 64 logical cores CPU (Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz) and one NVIDIA Tesla P100 GPU for acceleration. |
| Software Dependencies | No | All models are implemented using the Py Torch framework (Paszke et al., 2017). For O-TPP, as the authors Yang et al. (2017) have not published the code, we implement it using the tick (Bacry et al., 2017) library. All models are optimized using Adam (Kingma & Ba, 2015). |
| Experiment Setup | Yes | For NHP, the main hyperparameters to tune are the hidden dimension D... usually 32, 64, 128... For Att NHP, another important hyperparameter to tune is the number of layers L of the attention structure... usually 1, 2, 3, 4... we set α = 0.1 consistently for both datasets. For the prompts, we set M = 10, N = 4, Lp = 10 for both datasets. For the asynchronous training parameter C, we choose C = 2 for Taobao and Amazon datasets by default. Table 3 contains descriptions that list all of the hyperparameters set throughout our experiments. |