Summary of Dhp Benchmark: Are Llms Good Nlg Evaluators?, by Yicheng Wang et al.
DHP Benchmark: Are LLMs Good NLG Evaluators?
by Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang, Zhuoer Wang, Yingchi Liu, Mark Cusick, Param Kulkarni, Zhengping Ji, Yasser Ibrahim, Xia Hu
First submitted to arxiv on: 25 Aug 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 A novel approach to evaluating the capabilities of Large Language Models (LLMs) in Natural Language Generation (NLG) tasks is proposed, which addresses current limitations by leveraging a Discernment of Hierarchical Perturbation (DHP) benchmarking framework. This framework provides quantitative discernment scores for LLMs by using hierarchically perturbed text data and statistical tests to measure NLG evaluation capabilities. The framework is applied to six re-established evaluation datasets covering four NLG tasks: Summarization, Story Completion, Question Answering, and Translation. A comprehensive benchmarking of five major LLM families reveals their strengths and limitations as NLG evaluators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a way to test how well big language models are at judging the quality of writing generated by artificial intelligence systems. They created a special test that uses slightly changed versions of text to see how good the language models are at telling which one is better. The test was applied to six different datasets, covering four types of tasks: summarizing texts, completing stories, answering questions, and translating words. The results show how well each type of language model does in these tasks. |
Keywords
» Artificial intelligence » Language model » Question answering » Summarization » Translation
