Summary of Assessing Human Editing Effort on Llm-generated Texts Via Compression-based Edit Distance, by Nicolas Devatine and Louis Abraham
Assessing Human Editing Effort on LLM-Generated Texts via Compression-Based Edit Distance
by Nicolas Devatine, Louis Abraham
First submitted to arxiv on: 23 Dec 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 Assessing the human editing effort on Large Language Models (LLMs) is crucial for understanding AI-human interactions and improving text generation quality. Current edit distance metrics, such as Levenshtein, BLEU, ROUGE, and TER, often fail to accurately quantify the effort required, especially when edits involve substantial modifications like block operations. In this paper, we introduce a novel compression-based edit distance metric grounded in the Lempel-Ziv-77 algorithm, designed to measure the informational difference between original and edited texts. Our method leverages text compression properties to estimate post-editing time and effort. Experiments on real-world human edits datasets demonstrate high correlation with actual edit time and effort. We also show that LLMs exhibit an implicit understanding of editing speed aligning well with our metric. Additionally, we compare our metric with existing ones, highlighting its advantages in capturing complex edits with linear computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re using a computer program to generate text, and then you need to fix some mistakes. It’s hard to measure how much work goes into fixing those mistakes. Right now, there are ways to calculate this, but they don’t always get it right, especially when the changes are big. In this paper, scientists created a new way to measure the effort needed to edit text generated by these computer programs. They used an old algorithm called Lempel-Ziv-77 and applied it to how much information is changed between the original and edited texts. They tested their method on real examples of human editing and found that it accurately measures the time and effort spent editing. This new method can also help us understand how these computer programs work when they’re making text. |
Keywords
» Artificial intelligence » Bleu » Rouge » Text generation
