Summary of Check-eval: a Checklist-based Approach For Evaluating Text Quality, by Jayr Pereira and Andre Assumpcao and Roberto Lotufo
Check-Eval: A Checklist-based Approach for Evaluating Text Quality
by Jayr Pereira, Andre Assumpcao, Roberto Lotufo
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The evaluation of text quality generated by large language models (LLMs) remains a significant challenge. Traditional metrics often fail to align well with human judgments, particularly in tasks requiring creativity and nuance. This paper proposes Check-Eval, a novel evaluation framework leveraging LLMs to assess the quality of generated text through a checklist-based approach. The framework consists of two main stages: checklist generation and checklist evaluation. We validate Check-Eval on two benchmark datasets: Portuguese Legal Semantic Textual Similarity and SummEval. Our results demonstrate that Check-Eval achieves higher correlations with human judgments compared to existing metrics, such as G-Eval and GPTScore, underscoring its potential as a more reliable and effective evaluation framework for natural language generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to evaluate the quality of text written by artificial intelligence models. Right now, it’s hard to tell if the AI-generated text is good or not because our usual methods don’t match up with what humans think is good. The researchers came up with a new way to check the quality called Check-Eval. It uses other AI models to look at the text and make sure it meets certain standards. They tested this method on two sets of data and found that it worked better than other methods in making sure the AI-generated text was accurate and well-written. |
