Summary of Chatgpt Rates Natural Language Explanation Quality Like Humans: but on Which Scales?, by Fan Huang et al.
ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?
by Fan Huang, Haewoon Kwak, Kunwoo Park, Jisun An
First submitted to arxiv on: 26 Mar 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 study explores the alignment between ChatGPT’s natural language explanations and human assessments across multiple scales, aiming to evaluate the quality of AI-generated text explanations. Researchers sample 300 data instances from three datasets and collect 900 human annotations to assess informativeness and clarity. They find that ChatGPT aligns better with humans in coarser-grained scales and that paired comparisons and dynamic prompting can improve alignment. This research contributes to understanding large language models’ capabilities for responsible AI development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well a big computer program called ChatGPT explains its decisions, like how it makes judgments about text quality. They compared what the computer says with what humans think is good or bad about the same texts. The results show that the computer is better at this when making simple judgments, and gets even better if given examples of similar texts to work from. This helps us understand how computers can explain themselves in a way that’s fair and helpful. |
Keywords
» Artificial intelligence » Alignment » Prompting
