Summary of Every Answer Matters: Evaluating Commonsense with Probabilistic Measures, by Qi Cheng et al.
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures
by Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Lorraine Li
First submitted to arxiv on: 6 Jun 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 This paper tackles the issue of evaluating large language models’ common sense abilities by introducing a new generative task called commonsense frame completion (CFC). Unlike existing tasks that often rely on multiple-choice questions, CFC evaluates common sense through open-ended generations. The authors also propose a method for probabilistic evaluation that aligns well with human judgments. In this task, humans significantly outperform strong language model baselines, indicating the importance of evaluating machine common sense in a more comprehensive way. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can learn to have good common sense. Right now, tests for computer common sense are mostly multiple-choice questions that let computers cheat by looking for patterns. But real-life situations often involve many possible answers or no clear “right” answer. The researchers created a new test called commonsense frame completion (CFC) where computers generate text based on incomplete scenarios. They also came up with a way to score the computer’s answers that matches how humans judge them. Surprisingly, humans did much better than super-smart computer programs on this task. |
Keywords
» Artificial intelligence » Language model
