Evaluating TnT-LLM: Automatic, Human, and LLM-Based Assessment | HackerNoon

"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."

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.

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