Summary of A Step Towards Mixture Of Grader: Statistical Analysis Of Existing Automatic Evaluation Metrics, by Yun Joon Soh et al.
A Step Towards Mixture of Grader: Statistical Analysis of Existing Automatic Evaluation Metrics
by Yun Joon Soh, Jishen Zhao
First submitted to arxiv on: 13 Oct 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 investigates the limitations of existing evaluation metrics for Question-Answering (QA) models. By analyzing the correlation between different metrics and human-like evaluations, researchers found that while some metrics are highly correlated depending on question types, no single metric can accurately estimate human ratings. To address this issue, the authors propose a Mixture Of Grader approach to improve the quality of automated QA evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure we’re evaluating Question-Answering models correctly. Right now, there are many ways to measure how well these models do, but they might not all be fair or accurate. The researchers looked at how different methods of measuring performance compare to what humans think is a good answer. They found that while some methods work well for certain types of questions, no single method can get it right every time. To make things better, the authors suggest using a combination of these methods to give a more complete picture. |
Keywords
» Artificial intelligence » Question answering
