Summary of From Test-taking to Test-making: Examining Llm Authoring Of Commonsense Assessment Items, by Melissa Roemmele et al.
From Test-Taking to Test-Making: Examining LLM Authoring of Commonsense Assessment Items
by Melissa Roemmele, Andrew S. Gordon
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have demonstrated impressive capabilities in performing complex writing tasks, such as natural language inference and commonsense reasoning. This paper explores LLMs’ potential as authors of assessment items for commonsense reasoning, specifically drawing from the Choice of Plausible Alternatives (COPA) benchmark. The researchers prompt LLMs to generate items in the style of COPA, analyzing the results through human annotation and LLM-facilitated analysis. Surprisingly, they find that LLMs that excel at answering COPA questions are also successful in authoring their own items, highlighting the potential for LLMs as creators of assessment content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can write questions that test common sense and understanding. The researchers use a special set of questions called COPA to see if machines can create new questions like humans do. They found that machines that are good at answering COPA questions are also good at creating their own. This is important because it could help us develop new ways to test how well computers understand language. |
Keywords
» Artificial intelligence » Inference » Prompt
