Summary of Genres: Rethinking Evaluation For Generative Relation Extraction in the Era Of Large Language Models, by Pengcheng Jiang et al.
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
by Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun, Jiawei Han
First submitted to arxiv on: 16 Feb 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 addresses the limitations of traditional relation extraction (RE) metrics in evaluating generative relation extraction (GRE) methods. Current metrics rely on exact matching with human-annotated reference relations, which is insufficient for GRE’s diverse and semantically accurate outputs. The authors introduce GenRES, a multi-dimensional assessment tool that evaluates topic similarity, uniqueness, granularity, factualness, and completeness of GRE results. They demonstrate the effectiveness of GenRES by showing that traditional precision/recall metrics fail to justify GRE performance and that human-annotated referential relations can be incomplete. The paper also presents a comprehensive evaluation of 14 leading language models using GenRES across various RE datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how we can better evaluate machines that extract relationships from text. Right now, we use methods that don’t work well for new types of relationship extraction that are being developed. These new methods produce more accurate but different results than what humans have labeled as correct. The authors created a new way to measure the quality of these results, called GenRES, which looks at how similar the machine’s results are to human-annotated examples and other factors. They show that our current ways of measuring relationship extraction don’t work well for these new methods. |
Keywords
» Artificial intelligence » Precision » Recall
