The article discusses the challenges of evaluating unsupervised taxonomy generation and text classification due to the lack of standardized benchmarks. It introduces an evaluation suite for TnT-LLM that includes three categories of evaluation: deterministic automatic, human, and LLM-based evaluations. Each category has its strengths and weaknesses, and the aim is to leverage LLM evaluations in conjunction with human assessments for greater scalability and cost-effectiveness while addressing potential biases. This approach is designed to yield conclusions with statistical validity regarding taxonomy quality and utility.
The article proposes a novel evaluation suite for the TnT-LLM system, combining deterministic automatic evaluation, human evaluation, and LLM-based evaluations to address taxonomy generation challenges.
Given the unsupervised nature of the taxonomy generation task, traditional quantitative evaluation methods are inadequate; thus, we introduce a comprehensive suite to assess performance effectively.
#taxonomy-generation #text-classification #evaluation-methods #machine-learning #large-language-models
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