GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
This addresses a critical evaluation gap for researchers and practitioners in relation extraction, though it is incremental as it focuses on improving assessment rather than proposing a new method.
The paper tackled the problem that traditional metrics like precision and recall are inadequate for evaluating generative relation extraction (GRE) with large language models, as they rely on exact matching with references, and introduced GenRES for multi-dimensional assessment, showing it aligns with human preferences and benchmarking fourteen LLMs across datasets.
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs). However, we discovered that traditional relation extraction (RE) metrics like precision and recall fall short in evaluating GRE methods. This shortfall arises because these metrics rely on exact matching with human-annotated reference relations, while GRE methods often produce diverse and semantically accurate relations that differ from the references. To fill this gap, we introduce GenRES for a multi-dimensional assessment in terms of the topic similarity, uniqueness, granularity, factualness, and completeness of the GRE results. With GenRES, we empirically identified that (1) precision/recall fails to justify the performance of GRE methods; (2) human-annotated referential relations can be incomplete; (3) prompting LLMs with a fixed set of relations or entities can cause hallucinations. Next, we conducted a human evaluation of GRE methods that shows GenRES is consistent with human preferences for RE quality. Last, we made a comprehensive evaluation of fourteen leading LLMs using GenRES across document, bag, and sentence level RE datasets, respectively, to set the benchmark for future research in GRE