Summary of Defining and Detecting Vulnerability in Human Evaluation Guidelines: a Preliminary Study Towards Reliable Nlg Evaluation, by Jie Ruan et al.
Defining and Detecting Vulnerability in Human Evaluation Guidelines: A Preliminary Study Towards Reliable NLG Evaluation
by Jie Ruan, Wenqing Wang, Xiaojun Wan
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 new paper tackles the crucial issue of unreliable evaluation guidelines in Natural Language Generation (NLG) research. Despite human evaluation being the gold standard, many papers neglect to release their evaluation guidelines, leaving room for inaccuracies. The authors investigate this problem and find that only 29.84% of recent top-conference papers provide guidelines, with 77.09% of these exhibiting vulnerabilities. To address this challenge, they propose a novel approach by collecting annotations of existing guidelines and generating new ones using Large Language Models (LLMs). They also introduce a taxonomy of eight guideline vulnerabilities and offer recommendations for enhancing reliability in human evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has found that many papers on Natural Language Generation (NLG) don’t provide their evaluation guidelines, making it hard to compare results. This is a problem because NLG helps computers understand human language. To fix this, the authors created a new dataset with guidelines from existing papers and generated some using special computer models called Large Language Models (LLMs). They also developed a way to identify problems in these guidelines. The goal is to make it easier for other researchers to evaluate NLG systems accurately. |
