README

Paper link: https://arxiv.org/abs/2506.21605

Data location:

https://pan.baidu.com/s/1HqwY0nu5bltSAJ2TbnxcFQ?pwd=yzsj Extraction code: yzsj

or

https://drive.google.com/file/d/112Zraj4pTPH4Idph6i1uMOLA_LPFdGr0/view?usp=sharing

Data Details

We provide two versions of the data: Categorical data and data.

Categorical data represents data for each category under the first-person (Participation) and third-person (Observation), with category information and details available in Appendix Table 6.

The data represents the full datasets after categorization, as mentioned in the paper, including:

• Participation-Reflective (FirstAgentHighLevel)
• Participation-Factual (FirstAgentLowLevel)
• Observation-Reflective (ThirdAgentHighLevel)
• Observation-Factual (ThirdAgentLowLevel)

Noise data

We also provide a noise dataset, NoiseData, to extend the length of dialogues or information flows, including:

• FirstNoise (FirstAgent)
• ThirdNoise (ThirdAgent)

You can use makenoise.py to generate extended and sampled Complete Data. You can modify the main function in the source code to implement this. For each additional unit of noise length, the token count increases by about 1k on average.

You can refer to the following code:

MakeNoiseMessageHighLevel('data/ThirdAgentDataHighLevel.json', 'data2test', length=10, sample_num=100)  ## Add noise to the third-person high-level

MakeNoiseMessage('data/ThirdAgentDataLowLevel.json', 'data2test', length=10, sample_num=100)  ## Add noise to the third-person low-level

MakeNoiseSession('data/FirstAgentDataLowLevel.json', 'data2test', length=10, sample_num=100)  ## Add noise to the first-person low-level

MakeNoiseSession('data/FirstAgentDataHighLevel.json', 'data2test', length=10, sample_num=100)  ## Add noise to the first-person high-level