Abstract

Following the rapid progress in natural language processing (NLP) models, language models are applied to increasingly more complex interactive tasks such as negotiations and conversation moderations. Having human evaluators directly interact with these NLP models is essential for adequately evaluating the performance on such interactive tasks. We develop BotEval, an easily customizable, open-source, evaluation toolkit that focuses on enabling human-bot interactions as part of the evaluation process, as opposed to human evaluators making judgements for a static input. BotEval balances flexibility for customization and user-friendliness by providing templates for common use cases that span various degrees of complexity and built-in compatibility with popular crowdsourcing platforms. We showcase the numerous useful features of BotEval through a study that evaluates the performance of various chatbots on their effectiveness for conversational moderation and discuss how BotEval differs from other annotation tools.

Metadata

publication

ACL 2024 SDT, 2024

year

2024

publication date

2024

authors

Hyundong Cho, Thamme Gowda, Yuyang Huang, Zixun Lu, Tianli Tong, Jonathan May

link

https://arxiv.org/abs/2407.17770

resource_link

https://arxiv.org/pdf/2407.17770

journal

ACL 2024 SDT