Summary of Do Automatic Factuality Metrics Measure Factuality? a Critical Evaluation, by Sanjana Ramprasad et al.
Do Automatic Factuality Metrics Measure Factuality? A Critical Evaluation
by Sanjana Ramprasad, Byron C. Wallace
First submitted to arxiv on: 25 Nov 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 paper investigates the limitations of automatic factuality metrics used to evaluate the quality of abstractive summaries generated by Large Language Models (LLMs). Traditional evaluation metrics, such as ROUGE, have become saturated, but LLMs still introduce unwanted content, known as “hallucinations”, into their summaries. To address this issue, various metrics were developed to measure the factual consistency of generated summaries against their source. This paper questions whether these approaches accurately measure factuality and finds that a model using superficial features is competitive with state-of-the-art (SOTA) factuality scoring methods. The study also evaluates how factuality metrics respond to factual corrections in inconsistent summaries, finding that some metrics are more sensitive to benign edits than others. Moreover, the authors demonstrate that most automatic factuality metrics can be “gamed” by appending innocuous sentences to generated summaries, raising concerns about their reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well automated metrics work for measuring the quality of abstractive summaries made by Large Language Models (LLMs). Right now, traditional methods are too easy and don’t provide a good test. Sometimes these models add extra information that’s not supported by the original text. To fix this problem, new metrics were developed to see how well the generated summary matches its source. The researchers looked at how well these metrics work when the summary is corrected for mistakes. They found that some metrics are better than others and can even be tricked into thinking a bad summary is good if you add extra harmless information. This shows that we might not be able to trust these automated metrics as much as we thought. |
Keywords
» Artificial intelligence » Rouge
