Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2311.10781 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916235912937472 |
|---|---|
| author | Cho, Hyundong Liu, Shuai Shi, Taiwei Jain, Darpan Rizk, Basem Huang, Yuyang Lu, Zixun Wen, Nuan Gratch, Jonathan Ferrara, Emilio May, Jonathan |
| author_facet | Cho, Hyundong Liu, Shuai Shi, Taiwei Jain, Darpan Rizk, Basem Huang, Yuyang Lu, Zixun Wen, Nuan Gratch, Jonathan Ferrara, Emilio May, Jonathan |
| contents | Conversational moderation of online communities is crucial to maintaining civility for a constructive environment, but it is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier to aid human moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness grounded on moderation literature and establish design criteria for conducting realistic yet safe evaluation. We then propose a comprehensive evaluation framework to assess models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of language models as conversational moderators, finding that appropriately prompted models that incorporate insights from social science can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10781 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Can Language Model Moderators Improve the Health of Online Discourse? Cho, Hyundong Liu, Shuai Shi, Taiwei Jain, Darpan Rizk, Basem Huang, Yuyang Lu, Zixun Wen, Nuan Gratch, Jonathan Ferrara, Emilio May, Jonathan Computation and Language Artificial Intelligence Conversational moderation of online communities is crucial to maintaining civility for a constructive environment, but it is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier to aid human moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness grounded on moderation literature and establish design criteria for conducting realistic yet safe evaluation. We then propose a comprehensive evaluation framework to assess models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of language models as conversational moderators, finding that appropriately prompted models that incorporate insights from social science can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation. |
| title | Can Language Model Moderators Improve the Health of Online Discourse? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2311.10781 |